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            const closeToggle = () => {
              if (tocShowing) {
                toggleContainer.classList.remove("expanded");
                toggleContents.style.height = "0px";
                tocShowing = false;
              }
            };

            // Get rid of any expanded toggle if the user scrolls
            window.document.addEventListener(
              "scroll",
              throttle(() => {
                closeToggle();
              }, 50)
            );

            // Handle positioning of the toggle
            window.addEventListener(
              "resize",
              throttle(() => {
                elRect = undefined;
                positionToggle();
              }, 50)
            );

            window.addEventListener("quarto-hrChanged", () => {
              elRect = undefined;
            });

            // Process the click
            clickEl.onclick = () => {
              if (!tocShowing) {
                toggleContainer.classList.add("expanded");
                toggleContents.style.height = null;
                tocShowing = true;
              } else {
                closeToggle();
              }
            };
          });
        };

        // Converts a sidebar from a menu back to a sidebar
        const convertToSidebar = () => {
          for (const child of el.children) {
            child.style.opacity = 1;
            child.style.overflow = null;
          }

          const placeholderEl = window.document.getElementById(
            placeholderDescriptor.id
          );
          if (placeholderEl) {
            placeholderEl.remove();
          }

          el.classList.remove("rollup");
        };

        if (isReaderMode()) {
          convertToMenu();
          isVisible = false;
        } else {
          // Find the top and bottom o the element that is being managed
          const elTop = el.offsetTop;
          const elBottom =
            elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;

          if (!isVisible) {
            // If the element is current not visible reveal if there are
            // no conflicts with overlay regions
            if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
              convertToSidebar();
              isVisible = true;
            }
          } else {
            // If the element is visible, hide it if it conflicts with overlay regions
            // and insert a placeholder toggle (or if we're in reader mode)
            if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
              convertToMenu();
              isVisible = false;
            }
          }
        }
      }
    };
  };

  const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]');
  for (const tabEl of tabEls) {
    const id = tabEl.getAttribute("data-bs-target");
    if (id) {
      const columnEl = document.querySelector(
        `${id} .column-margin, .tabset-margin-content`
      );
      if (columnEl)
        tabEl.addEventListener("shown.bs.tab", function (event) {
          const el = event.srcElement;
          if (el) {
            const visibleCls = `${el.id}-margin-content`;
            // walk up until we find a parent tabset
            let panelTabsetEl = el.parentElement;
            while (panelTabsetEl) {
              if (panelTabsetEl.classList.contains("panel-tabset")) {
                break;
              }
              panelTabsetEl = panelTabsetEl.parentElement;
            }

            if (panelTabsetEl) {
              const prevSib = panelTabsetEl.previousElementSibling;
              if (
                prevSib &&
                prevSib.classList.contains("tabset-margin-container")
              ) {
                const childNodes = prevSib.querySelectorAll(
                  ".tabset-margin-content"
                );
                for (const childEl of childNodes) {
                  if (childEl.classList.contains(visibleCls)) {
                    childEl.classList.remove("collapse");
                  } else {
                    childEl.classList.add("collapse");
                  }
                }
              }
            }
          }

          layoutMarginEls();
        });
    }
  }

  // Manage the visibility of the toc and the sidebar
  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
    id: "quarto-toc-toggle",
    titleSelector: "#toc-title",
    dismissOnClick: true,
  });
  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
    id: "quarto-sidebarnav-toggle",
    titleSelector: ".title",
    dismissOnClick: false,
  });
  let tocLeftScrollVisibility;
  if (leftTocEl) {
    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
      id: "quarto-lefttoc-toggle",
      titleSelector: "#toc-title",
      dismissOnClick: true,
    });
  }

  // Find the first element that uses formatting in special columns
  const conflictingEls = window.document.body.querySelectorAll(
    '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
  );

  // Filter all the possibly conflicting elements into ones
  // the do conflict on the left or ride side
  const arrConflictingEls = Array.from(conflictingEls);
  const leftSideConflictEls = arrConflictingEls.filter((el) => {
    if (el.tagName === "ASIDE") {
      return false;
    }
    return Array.from(el.classList).find((className) => {
      return (
        className !== "column-body" &&
        className.startsWith("column-") &&
        !className.endsWith("right") &&
        !className.endsWith("container") &&
        className !== "column-margin"
      );
    });
  });
  const rightSideConflictEls = arrConflictingEls.filter((el) => {
    if (el.tagName === "ASIDE") {
      return true;
    }

    const hasMarginCaption = Array.from(el.classList).find((className) => {
      return className == "margin-caption";
    });
    if (hasMarginCaption) {
      return true;
    }

    return Array.from(el.classList).find((className) => {
      return (
        className !== "column-body" &&
        !className.endsWith("container") &&
        className.startsWith("column-") &&
        !className.endsWith("left")
      );
    });
  });

  const kOverlapPaddingSize = 10;
  function toRegions(els) {
    return els.map((el) => {
      const boundRect = el.getBoundingClientRect();
      const top =
        boundRect.top +
        document.documentElement.scrollTop -
        kOverlapPaddingSize;
      return {
        top,
        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
      };
    });
  }

  let hasObserved = false;
  const visibleItemObserver = (els) => {
    let visibleElements = [...els];
    const intersectionObserver = new IntersectionObserver(
      (entries, _observer) => {
        entries.forEach((entry) => {
          if (entry.isIntersecting) {
            if (visibleElements.indexOf(entry.target) === -1) {
              visibleElements.push(entry.target);
            }
          } else {
            visibleElements = visibleElements.filter((visibleEntry) => {
              return visibleEntry !== entry;
            });
          }
        });

        if (!hasObserved) {
          hideOverlappedSidebars();
        }
        hasObserved = true;
      },
      {}
    );
    els.forEach((el) => {
      intersectionObserver.observe(el);
    });

    return {
      getVisibleEntries: () => {
        return visibleElements;
      },
    };
  };

  const rightElementObserver = visibleItemObserver(rightSideConflictEls);
  const leftElementObserver = visibleItemObserver(leftSideConflictEls);

  const hideOverlappedSidebars = () => {
    marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
    sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
    if (tocLeftScrollVisibility) {
      tocLeftScrollVisibility(
        toRegions(leftElementObserver.getVisibleEntries())
      );
    }
  };

  window.quartoToggleReader = () => {
    // Applies a slow class (or removes it)
    // to update the transition speed
    const slowTransition = (slow) => {
      const manageTransition = (id, slow) => {
        const el = document.getElementById(id);
        if (el) {
          if (slow) {
            el.classList.add("slow");
          } else {
            el.classList.remove("slow");
          }
        }
      };

      manageTransition("TOC", slow);
      manageTransition("quarto-sidebar", slow);
    };
    const readerMode = !isReaderMode();
    setReaderModeValue(readerMode);

    // If we're entering reader mode, slow the transition
    if (readerMode) {
      slowTransition(readerMode);
    }
    highlightReaderToggle(readerMode);
    hideOverlappedSidebars();

    // If we're exiting reader mode, restore the non-slow transition
    if (!readerMode) {
      slowTransition(!readerMode);
    }
  };

  const highlightReaderToggle = (readerMode) => {
    const els = document.querySelectorAll(".quarto-reader-toggle");
    if (els) {
      els.forEach((el) => {
        if (readerMode) {
          el.classList.add("reader");
        } else {
          el.classList.remove("reader");
        }
      });
    }
  };

  const setReaderModeValue = (val) => {
    if (window.location.protocol !== "file:") {
      window.localStorage.setItem("quarto-reader-mode", val);
    } else {
      localReaderMode = val;
    }
  };

  const isReaderMode = () => {
    if (window.location.protocol !== "file:") {
      return window.localStorage.getItem("quarto-reader-mode") === "true";
    } else {
      return localReaderMode;
    }
  };
  let localReaderMode = null;

  const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded");
  const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;

  // Walk the TOC and collapse/expand nodes
  // Nodes are expanded if:
  // - they are top level
  // - they have children that are 'active' links
  // - they are directly below an link that is 'active'
  const walk = (el, depth) => {
    // Tick depth when we enter a UL
    if (el.tagName === "UL") {
      depth = depth + 1;
    }

    // It this is active link
    let isActiveNode = false;
    if (el.tagName === "A" && el.classList.contains("active")) {
      isActiveNode = true;
    }

    // See if there is an active child to this element
    let hasActiveChild = false;
    for (child of el.children) {
      hasActiveChild = walk(child, depth) || hasActiveChild;
    }

    // Process the collapse state if this is an UL
    if (el.tagName === "UL") {
      if (tocOpenDepth === -1 && depth > 1) {
        el.classList.add("collapse");
      } else if (
        depth <= tocOpenDepth ||
        hasActiveChild ||
        prevSiblingIsActiveLink(el)
      ) {
        el.classList.remove("collapse");
      } else {
        el.classList.add("collapse");
      }

      // untick depth when we leave a UL
      depth = depth - 1;
    }
    return hasActiveChild || isActiveNode;
  };

  // walk the TOC and expand / collapse any items that should be shown

  if (tocEl) {
    walk(tocEl, 0);
    updateActiveLink();
  }

  // Throttle the scroll event and walk peridiocally
  window.document.addEventListener(
    "scroll",
    throttle(() => {
      if (tocEl) {
        updateActiveLink();
        walk(tocEl, 0);
      }
      if (!isReaderMode()) {
        hideOverlappedSidebars();
      }
    }, 5)
  );
  window.addEventListener(
    "resize",
    throttle(() => {
      if (!isReaderMode()) {
        hideOverlappedSidebars();
      }
    }, 10)
  );
  hideOverlappedSidebars();
  highlightReaderToggle(isReaderMode());
});

// grouped tabsets
window.addEventListener("pageshow", (_event) => {
  function getTabSettings() {
    const data = localStorage.getItem("quarto-persistent-tabsets-data");
    if (!data) {
      localStorage.setItem("quarto-persistent-tabsets-data", "{}");
      return {};
    }
    if (data) {
      return JSON.parse(data);
    }
  }

  function setTabSettings(data) {
    localStorage.setItem(
      "quarto-persistent-tabsets-data",
      JSON.stringify(data)
    );
  }

  function setTabState(groupName, groupValue) {
    const data = getTabSettings();
    data[groupName] = groupValue;
    setTabSettings(data);
  }

  function toggleTab(tab, active) {
    const tabPanelId = tab.getAttribute("aria-controls");
    const tabPanel = document.getElementById(tabPanelId);
    if (active) {
      tab.classList.add("active");
      tabPanel.classList.add("active");
    } else {
      tab.classList.remove("active");
      tabPanel.classList.remove("active");
    }
  }

  function toggleAll(selectedGroup, selectorsToSync) {
    for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
      const active = selectedGroup === thisGroup;
      for (const tab of tabs) {
        toggleTab(tab, active);
      }
    }
  }

  function findSelectorsToSyncByLanguage() {
    const result = {};
    const tabs = Array.from(
      document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
    );
    for (const item of tabs) {
      const div = item.parentElement.parentElement.parentElement;
      const group = div.getAttribute("data-group");
      if (!result[group]) {
        result[group] = {};
      }
      const selectorsToSync = result[group];
      const value = item.innerHTML;
      if (!selectorsToSync[value]) {
        selectorsToSync[value] = [];
      }
      selectorsToSync[value].push(item);
    }
    return result;
  }

  function setupSelectorSync() {
    const selectorsToSync = findSelectorsToSyncByLanguage();
    Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
      Object.entries(tabSetsByValue).forEach(([value, items]) => {
        items.forEach((item) => {
          item.addEventListener("click", (_event) => {
            setTabState(group, value);
            toggleAll(value, selectorsToSync[group]);
          });
        });
      });
    });
    return selectorsToSync;
  }

  const selectorsToSync = setupSelectorSync();
  for (const [group, selectedName] of Object.entries(getTabSettings())) {
    const selectors = selectorsToSync[group];
    // it's possible that stale state gives us empty selections, so we explicitly check here.
    if (selectors) {
      toggleAll(selectedName, selectors);
    }
  }
});

function throttle(func, wait) {
  let waiting = false;
  return function () {
    if (!waiting) {
      func.apply(this, arguments);
      waiting = true;
      setTimeout(function () {
        waiting = false;
      }, wait);
    }
  };
}

function nexttick(func) {
  return setTimeout(func, 0);
}
</script>
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s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function mi(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?fi:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},fi,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof 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vi=Object.freeze(Object.defineProperty({__proto__:null,afterMain:ae,afterRead:se,afterWrite:he,applyStyles:_e,arrow:je,auto:Kt,basePlacements:Qt,beforeMain:oe,beforeRead:ie,beforeWrite:le,bottom:Rt,clippingParents:Ut,computeStyles:Be,createPopper:bi,createPopperBase:gi,createPopperLite:_i,detectOverflow:ii,end:Yt,eventListeners:Re,flip:si,hide:ai,left:Vt,main:re,modifierPhases:de,offset:li,placements:ee,popper:Jt,popperGenerator:mi,popperOffsets:ci,preventOverflow:hi,read:ne,reference:Zt,right:qt,start:Xt,top:zt,variationPlacements:te,viewport:Gt,write:ce},Symbol.toStringTag,{value:"Module"})),yi="dropdown",wi=".bs.dropdown",Ai=".data-api",Ei="ArrowUp",Ti="ArrowDown",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ai}`,Si=`keydown${wi}${Ai}`,Di=`keyup${wi}${Ai}`,$i="show",Ii='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=".dropdown-menu",Mi=p()?"top-end":"top-start",ji=p()?"top-start":"top-end",Fi=p()?"bottom-end":"bottom-start",Hi=p()?"bottom-start":"bottom-end",Wi=p()?"left-start":"right-start",Bi=p()?"right-start":"left-start",zi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class qi extends W{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=z.next(this._element,Pi)[0]||z.prev(this._element,Pi)[0]||z.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(l(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!N.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add($i),this._element.classList.add($i),N.trigger(this._element,ki,t)}}hide(){if(l(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!N.trigger(this._element,Ci,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute("aria-expanded","false"),F.removeDataAttribute(this._menu,"popper"),N.trigger(this._element,Oi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===vi)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:o(this._config.reference)?t=r(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=bi(t,this._menu,e)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Wi;if(t.classList.contains("dropstart"))return Bi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ji:Mi:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=z.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return 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) format("woff");
}
.bi::before,
[class^="bi-"]::before,
[class*=" bi-"]::before {
display: inline-block;
font-family: bootstrap-icons !important;
font-style: normal;
font-weight: normal !important;
font-variant: normal;
text-transform: none;
line-height: 1;
vertical-align: -.125em;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
.bi-123::before { content: "\f67f"; }
.bi-alarm-fill::before { content: "\f101"; }
.bi-alarm::before { content: "\f102"; }
.bi-align-bottom::before { content: "\f103"; }
.bi-align-center::before { content: "\f104"; }
.bi-align-end::before { content: "\f105"; }
.bi-align-middle::before { content: "\f106"; }
.bi-align-start::before { content: "\f107"; }
.bi-align-top::before { content: "\f108"; }
.bi-alt::before { content: "\f109"; }
.bi-app-indicator::before { content: "\f10a"; }
.bi-app::before { content: "\f10b"; }
.bi-archive-fill::before { content: "\f10c"; }
.bi-archive::before { content: "\f10d"; }
.bi-arrow-90deg-down::before { content: "\f10e"; }
.bi-arrow-90deg-left::before { content: "\f10f"; }
.bi-arrow-90deg-right::before { content: "\f110"; }
.bi-arrow-90deg-up::before { content: "\f111"; }
.bi-arrow-bar-down::before { content: "\f112"; }
.bi-arrow-bar-left::before { content: "\f113"; }
.bi-arrow-bar-right::before { content: "\f114"; }
.bi-arrow-bar-up::before { content: "\f115"; }
.bi-arrow-clockwise::before { content: "\f116"; }
.bi-arrow-counterclockwise::before { content: "\f117"; }
.bi-arrow-down-circle-fill::before { content: "\f118"; }
.bi-arrow-down-circle::before { content: "\f119"; }
.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
.bi-arrow-down-left-circle::before { content: "\f11b"; }
.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
.bi-arrow-down-left-square::before { content: "\f11d"; }
.bi-arrow-down-left::before { content: "\f11e"; }
.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
.bi-arrow-down-right-circle::before { content: "\f120"; }
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.bi-suitcase-fill::before { content: "\f8fd"; }
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.bi-suitcase2-fill::before { content: "\f901"; }
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.bi-vignette::before { content: "\f903"; }
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<div id="quarto-content" class="page-columns page-rows-contents page-layout-article">
<div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
  <nav id="TOC" role="doc-toc" class="toc-active">
    <h2 id="toc-title">Table of contents</h2>
   
  <ul>
  <li><a href="#r-setup" id="toc-r-setup" class="nav-link active" data-scroll-target="#r-setup"><span class="header-section-number">1</span> R Setup</a></li>
  <li><a href="#time-to-complete-block-data" id="toc-time-to-complete-block-data" class="nav-link" data-scroll-target="#time-to-complete-block-data"><span class="header-section-number">2</span> Time to Complete Block Data</a>
  <ul class="collapse">
  <li><a href="#codebook-for-tcb-data" id="toc-codebook-for-tcb-data" class="nav-link" data-scroll-target="#codebook-for-tcb-data"><span class="header-section-number">2.1</span> Codebook for TCB data</a></li>
  <li><a href="#current-analytic-goals-for-tcb" id="toc-current-analytic-goals-for-tcb" class="nav-link" data-scroll-target="#current-analytic-goals-for-tcb"><span class="header-section-number">2.2</span> Current analytic goals for TCB</a></li>
  </ul></li>
  <li><a href="#tcb-vs.-abs_block-overall" id="toc-tcb-vs.-abs_block-overall" class="nav-link" data-scroll-target="#tcb-vs.-abs_block-overall"><span class="header-section-number">3</span> TCB vs. <code>abs_block</code>, overall</a>
  <ul class="collapse">
  <li><a href="#plot-1-linear-fit" id="toc-plot-1-linear-fit" class="nav-link" data-scroll-target="#plot-1-linear-fit"><span class="header-section-number">3.1</span> Plot 1 (Linear Fit)</a></li>
  <li><a href="#new-plot-1a.-grouped" id="toc-new-plot-1a.-grouped" class="nav-link" data-scroll-target="#new-plot-1a.-grouped"><span class="header-section-number">3.2</span> NEW! Plot 1A. (Grouped)</a></li>
  <li><a href="#plot-1b.-quadratic-fit" id="toc-plot-1b.-quadratic-fit" class="nav-link" data-scroll-target="#plot-1b.-quadratic-fit"><span class="header-section-number">3.3</span> Plot 1B. (Quadratic Fit)</a></li>
  <li><a href="#plot-1c.-comparing-the-linear-to-the-quadratic-fit" id="toc-plot-1c.-comparing-the-linear-to-the-quadratic-fit" class="nav-link" data-scroll-target="#plot-1c.-comparing-the-linear-to-the-quadratic-fit"><span class="header-section-number">3.4</span> Plot 1C. Comparing the Linear to the Quadratic Fit</a></li>
  <li><a href="#conclusions" id="toc-conclusions" class="nav-link" data-scroll-target="#conclusions"><span class="header-section-number">3.5</span> Conclusions</a></li>
  <li><a href="#new-plot-1d.-histogram" id="toc-new-plot-1d.-histogram" class="nav-link" data-scroll-target="#new-plot-1d.-histogram"><span class="header-section-number">3.6</span> NEW! Plot 1D. (Histogram)</a></li>
  </ul></li>
  <li><a href="#tcb-stratified-by-of-instantaneous-bt-groups" id="toc-tcb-stratified-by-of-instantaneous-bt-groups" class="nav-link" data-scroll-target="#tcb-stratified-by-of-instantaneous-bt-groups"><span class="header-section-number">4</span> TCB stratified by % of instantaneous BT groups</a>
  <ul class="collapse">
  <li><a href="#plot-2-time-to-block-vs.-instantaneous-bt-by-bt" id="toc-plot-2-time-to-block-vs.-instantaneous-bt-by-bt" class="nav-link" data-scroll-target="#plot-2-time-to-block-vs.-instantaneous-bt-by-bt"><span class="header-section-number">4.1</span> Plot 2 (time to block vs. instantaneous BT, by % BT)</a></li>
  <li><a href="#model-for-plot-2" id="toc-model-for-plot-2" class="nav-link" data-scroll-target="#model-for-plot-2"><span class="header-section-number">4.2</span> Model for Plot 2</a></li>
  <li><a href="#what-is-the-effect-of-adding-pct_ib-to-a-model-with-just-inst_bt" id="toc-what-is-the-effect-of-adding-pct_ib-to-a-model-with-just-inst_bt" class="nav-link" data-scroll-target="#what-is-the-effect-of-adding-pct_ib-to-a-model-with-just-inst_bt"><span class="header-section-number">4.3</span> What is the effect of adding <code>pct_ib</code> to a model with just <code>inst_bt</code>?</a></li>
  <li><a href="#new-r-square-values-for-predicting-tcb" id="toc-new-r-square-values-for-predicting-tcb" class="nav-link" data-scroll-target="#new-r-square-values-for-predicting-tcb"><span class="header-section-number">4.4</span> NEW! R-square values for predicting TCB</a></li>
  <li><a href="#model-treating-both-inst_bt-and-pct_ib-as-numeric" id="toc-model-treating-both-inst_bt-and-pct_ib-as-numeric" class="nav-link" data-scroll-target="#model-treating-both-inst_bt-and-pct_ib-as-numeric"><span class="header-section-number">4.5</span> Model treating both <code>inst_bt</code> and <code>pct_ib</code> as numeric</a></li>
  <li><a href="#plot-3-including-the-cases-where-complete-block-was-not-achieved" id="toc-plot-3-including-the-cases-where-complete-block-was-not-achieved" class="nav-link" data-scroll-target="#plot-3-including-the-cases-where-complete-block-was-not-achieved"><span class="header-section-number">4.6</span> Plot 3 (<em>including</em> the cases where complete block was not achieved)</a></li>
  <li><a href="#plot-3a-excluding-the-cases-where-complete-block-was-not-achieved" id="toc-plot-3a-excluding-the-cases-where-complete-block-was-not-achieved" class="nav-link" data-scroll-target="#plot-3a-excluding-the-cases-where-complete-block-was-not-achieved"><span class="header-section-number">4.7</span> Plot 3A (<em>excluding</em> the cases where complete block was not achieved)</a></li>
  <li><a href="#model-for-plot-3a" id="toc-model-for-plot-3a" class="nav-link" data-scroll-target="#model-for-plot-3a"><span class="header-section-number">4.8</span> Model for Plot 3A</a></li>
  </ul></li>
  <li><a href="#time-to-complete-recovery-data" id="toc-time-to-complete-recovery-data" class="nav-link" data-scroll-target="#time-to-complete-recovery-data"><span class="header-section-number">5</span> Time to Complete Recovery Data</a>
  <ul class="collapse">
  <li><a href="#codebook-for-tcr-data" id="toc-codebook-for-tcr-data" class="nav-link" data-scroll-target="#codebook-for-tcr-data"><span class="header-section-number">5.1</span> Codebook for TCR data</a></li>
  <li><a href="#current-analytic-goals-for-tcr" id="toc-current-analytic-goals-for-tcr" class="nav-link" data-scroll-target="#current-analytic-goals-for-tcr"><span class="header-section-number">5.2</span> Current analytic goals for TCR</a></li>
  </ul></li>
  <li><a href="#tcr-vs.-charge-overall" id="toc-tcr-vs.-charge-overall" class="nav-link" data-scroll-target="#tcr-vs.-charge-overall"><span class="header-section-number">6</span> TCR vs. <code>charge</code>, overall</a>
  <ul class="collapse">
  <li><a href="#plot-4-linear-fit" id="toc-plot-4-linear-fit" class="nav-link" data-scroll-target="#plot-4-linear-fit"><span class="header-section-number">6.1</span> Plot 4 (Linear Fit)</a></li>
  <li><a href="#new-plot-4a.-grouped" id="toc-new-plot-4a.-grouped" class="nav-link" data-scroll-target="#new-plot-4a.-grouped"><span class="header-section-number">6.2</span> NEW! Plot 4A. (Grouped)</a></li>
  <li><a href="#plot-4b.-quadratic-fit" id="toc-plot-4b.-quadratic-fit" class="nav-link" data-scroll-target="#plot-4b.-quadratic-fit"><span class="header-section-number">6.3</span> Plot 4B. (Quadratic Fit)</a></li>
  <li><a href="#plot-4c.-comparing-the-linear-to-the-quadratic-fit" id="toc-plot-4c.-comparing-the-linear-to-the-quadratic-fit" class="nav-link" data-scroll-target="#plot-4c.-comparing-the-linear-to-the-quadratic-fit"><span class="header-section-number">6.4</span> Plot 4C. Comparing the Linear to the Quadratic Fit</a></li>
  <li><a href="#conclusions-1" id="toc-conclusions-1" class="nav-link" data-scroll-target="#conclusions-1"><span class="header-section-number">6.5</span> Conclusions</a></li>
  </ul></li>
  <li><a href="#tcr-stratified-by-block-period-groups" id="toc-tcr-stratified-by-block-period-groups" class="nav-link" data-scroll-target="#tcr-stratified-by-block-period-groups"><span class="header-section-number">7</span> TCR stratified by Block Period groups</a>
  <ul class="collapse">
  <li><a href="#plot-5-time-to-recover-vs.-bt-by-block-period" id="toc-plot-5-time-to-recover-vs.-bt-by-block-period" class="nav-link" data-scroll-target="#plot-5-time-to-recover-vs.-bt-by-block-period"><span class="header-section-number">7.1</span> Plot 5 (time to recover vs. BT, by block period)</a></li>
  <li><a href="#model-for-plot-5" id="toc-model-for-plot-5" class="nav-link" data-scroll-target="#model-for-plot-5"><span class="header-section-number">7.2</span> Model for Plot 5</a></li>
  <li><a href="#what-is-the-effect-of-adding-block_period-to-a-model-with-just-bt" id="toc-what-is-the-effect-of-adding-block_period-to-a-model-with-just-bt" class="nav-link" data-scroll-target="#what-is-the-effect-of-adding-block_period-to-a-model-with-just-bt"><span class="header-section-number">7.3</span> What is the effect of adding <code>block_period</code> to a model with just <code>bt</code>?</a></li>
  <li><a href="#new-r-square-values-for-predicting-tcr" id="toc-new-r-square-values-for-predicting-tcr" class="nav-link" data-scroll-target="#new-r-square-values-for-predicting-tcr"><span class="header-section-number">7.4</span> NEW! R-square values for predicting TCR</a></li>
  <li><a href="#model-treating-both-bt-and-block_period-as-numeric" id="toc-model-treating-both-bt-and-block_period-as-numeric" class="nav-link" data-scroll-target="#model-treating-both-bt-and-block_period-as-numeric"><span class="header-section-number">7.5</span> Model treating both <code>bt</code> and <code>block_period</code> as numeric</a></li>
  </ul></li>
  </ul>
</nav>
</div>
<main class="content" id="quarto-document-content">

<header id="title-block-header" class="quarto-title-block default">
<div class="quarto-title">
<div class="quarto-title-block"><div><h1 class="title">Time to Complete Block and Time to Recovery Analyses</h1><button type="button" class="btn code-tools-button dropdown-toggle" id="quarto-code-tools-menu" data-bs-toggle="dropdown" aria-expanded="false"><i class="bi"></i> Code</button><ul class="dropdown-menu dropdown-menu-end" aria-labelelledby="quarto-code-tools-menu"><li><a id="quarto-show-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Show All Code</a></li><li><a id="quarto-hide-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Hide All Code</a></li><li><hr class="dropdown-divider"></li><li><a id="quarto-view-source" class="dropdown-item" href="javascript:void(0)" role="button">View Source</a></li></ul></div></div>
<p class="subtitle lead">EMG Reflex Induction Recovery</p>
</div>



<div class="quarto-title-meta">

    <div>
    <div class="quarto-title-meta-heading">Author</div>
    <div class="quarto-title-meta-contents">
             <p>Thomas E. Love </p>
          </div>
  </div>
    
    <div>
    <div class="quarto-title-meta-heading">Published</div>
    <div class="quarto-title-meta-contents">
      <p class="date">2024-06-03 12:45</p>
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</header>


<section id="r-setup" class="level1" data-number="1">
<h1 data-number="1"><span class="header-section-number">1</span> R Setup</h1>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(janitor)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(knitr)</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(broom)</span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(glue)</span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lme4)</span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(mosaic)</span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(naniar)</span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lubridate)</span>
<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_bw</span>())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="time-to-complete-block-data" class="level1" data-number="2">
<h1 data-number="2"><span class="header-section-number">2</span> Time to Complete Block Data</h1>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>tcb <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(<span class="st">&quot;EMGReflex_Induction_Recovery_TCB-2024-05-27-revised.csv&quot;</span>, </span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>                <span class="at">show_col_types =</span> F) <span class="sc">|&gt;</span> </span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">clean_names</span>() <span class="sc">|&gt;</span></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">date =</span> <span class="fu">mdy</span>(date)) <span class="sc">|&gt;</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">pct_ib =</span> <span class="fu">factor</span>(pct_ib),</span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a>         <span class="at">set =</span> <span class="fu">factor</span>(set),</span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a>         <span class="at">animal =</span> <span class="fu">factor</span>(animal))</span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a>tcb_b <span class="ot">&lt;-</span> tcb <span class="sc">|&gt;</span> <span class="fu">filter</span>(time_block <span class="sc">&lt;</span> <span class="dv">300</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
<section id="codebook-for-tcb-data" class="level2" data-number="2.1">
<h2 data-number="2.1" class="anchored" data-anchor-id="codebook-for-tcb-data"><span class="header-section-number">2.1</span> Codebook for TCB data</h2>
<blockquote class="blockquote">
<p>Block amplitudes of either 40, 60, or 80% of the instantaneous block threshold were applied continuously until complete block was achieved, at which point the block was turned off. The Time to Complete Block (TCB) was recorded in seconds.</p>
</blockquote>
<p>In the original TCB data, we have 48 rows and 7 variables, with 0 missing observations.</p>
<p>The data capture 7 distinct animals. Each “run” is identified by an <code>animal</code>, a <code>set</code> (1, 2, or 3) and <code>date</code>. Each <code>animal</code>-<code>set</code> combination yields three runs, all done on the same <code>date</code>, with the same <code>inst_bt</code>, at the three levels of the experimental setting <code>pct_ib</code>, yielding an <code>abs_block</code> value specific to the <code>animal</code> in that <code>set</code> on that <code>date</code>.</p>
<p>The outcome of interest is time to complete block, measured in seconds. Values of the time to complete block of 300 indicate that complete block was not achieved in 5 minutes.</p>
<p>There are 38 experiments (rows) where the time to complete block was recorded (as less than 300 seconds), and we will restrict some analyses to these experiments.</p>
<table class="table">
<colgroup>
<col style="width: 17%">
<col style="width: 82%">
</colgroup>
<thead>
<tr class="header">
<th style="text-align: center;">Variable</th>
<th style="text-align: left;">Description</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: center;"><code>animal</code></td>
<td style="text-align: left;">7 different animals</td>
</tr>
<tr class="even">
<td style="text-align: center;"><code>date</code></td>
<td style="text-align: left;">between 2022-08-26 and 2023-05-23</td>
</tr>
<tr class="odd">
<td style="text-align: center;"><code>set</code></td>
<td style="text-align: left;">1, 2 or 3 (consistent in <code>date</code>)</td>
</tr>
<tr class="even">
<td style="text-align: center;"><code>inst_bt</code></td>
<td style="text-align: left;">instantaneous BT (mA): consistent within <code>set</code> for each <code>animal</code></td>
</tr>
<tr class="odd">
<td style="text-align: center;"><code>pct_ib</code></td>
<td style="text-align: left;">% of instantaneous BT: experimental setting: 40, 60 or 80</td>
</tr>
<tr class="even">
<td style="text-align: center;"><code>abs_block</code></td>
<td style="text-align: left;">absolute block value (mA): product of <code>inst_bt</code> and <code>pct_ib</code></td>
</tr>
<tr class="odd">
<td style="text-align: center;"><code>time_block</code></td>
<td style="text-align: left;">time to complete block in seconds: quantitative between 2 and 300, with all quantities ending in 2 except for the values originally listed as &gt;300 (here stored as 300.)</td>
</tr>
</tbody>
</table>
</section>
<section id="current-analytic-goals-for-tcb" class="level2" data-number="2.2">
<h2 data-number="2.2" class="anchored" data-anchor-id="current-analytic-goals-for-tcb"><span class="header-section-number">2.2</span> Current analytic goals for TCB</h2>
<p>After restricting the TCB data to those 38 experiments where complete block was achieved in less than 300 seconds,</p>
<ol type="1">
<li>Is time to complete block (<code>time_block</code>) reasonably well-described using a linear function of <code>abs_block</code>?</li>
<li>If that’s true, then which of the two elements: <code>inst_bt</code> or <code>pct_ib</code> (whose product is equal to <code>abs_block</code>) is the driver of that effect? Or is it truly the combination?</li>
</ol>
</section>
</section>
<section id="tcb-vs.-abs_block-overall" class="level1" data-number="3">
<h1 data-number="3"><span class="header-section-number">3</span> TCB vs. <code>abs_block</code>, overall</h1>
<p>Is time to complete block (<code>time_block</code>) reasonably well-described using a linear function of <code>abs_block</code>?</p>
<section id="plot-1-linear-fit" class="level2" data-number="3.1">
<h2 data-number="3.1" class="anchored" data-anchor-id="plot-1-linear-fit"><span class="header-section-number">3.1</span> Plot 1 (Linear Fit)</h2>
<p>This plot excludes the experiments where <code>time_block</code> is 300 seconds or more.</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcb_b, <span class="fu">aes</span>(<span class="at">x =</span> abs_block, <span class="at">y =</span> time_block)) <span class="sc">+</span></span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, <span class="at">formula =</span> y <span class="sc">~</span> x) <span class="sc">+</span></span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Block, by Absolute Block Value&quot;</span>,</span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">glue</span>(<span class="fu">nrow</span>(tcb_b), <span class="st">&quot; experiments which achieved complete block&quot;</span>),</span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Absolute Block Value (mA)&quot;</span>,</span>
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Block (seconds)&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img 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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot1.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
<p>The linear model shown here is summarized below:</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>m1 <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_block <span class="sc">~</span> abs_block, <span class="at">data =</span> tcb_b)</span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>m1t <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m1, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m1t, <span class="at">digits =</span> <span class="dv">3</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: left;">term</th>
<th style="text-align: right;">estimate</th>
<th style="text-align: right;">std.error</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">conf.low</th>
<th style="text-align: right;">conf.high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">(Intercept)</td>
<td style="text-align: right;">160.806</td>
<td style="text-align: right;">19.591</td>
<td style="text-align: right;">0</td>
<td style="text-align: right;">121.074</td>
<td style="text-align: right;">200.538</td>
</tr>
<tr class="even">
<td style="text-align: left;">abs_block</td>
<td style="text-align: right;">-64.644</td>
<td style="text-align: right;">14.398</td>
<td style="text-align: right;">0</td>
<td style="text-align: right;">-93.846</td>
<td style="text-align: right;">-35.443</td>
</tr>
</tbody>
</table>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m1) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: right;">r.squared</th>
<th style="text-align: right;">adj.r.squared</th>
<th style="text-align: right;">sigma</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">df</th>
<th style="text-align: right;">df.residual</th>
<th style="text-align: right;">nobs</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: right;">0.3589405</td>
<td style="text-align: right;">0.3411333</td>
<td style="text-align: right;">59.87462</td>
<td style="text-align: right;">7.06e-05</td>
<td style="text-align: right;">1</td>
<td style="text-align: right;">36</td>
<td style="text-align: right;">38</td>
</tr>
</tbody>
</table>
</div>
</div>
</section>
<section id="new-plot-1a.-grouped" class="level2" data-number="3.2">
<h2 data-number="3.2" class="anchored" data-anchor-id="new-plot-1a.-grouped"><span class="header-section-number">3.2</span> NEW! Plot 1A. (Grouped)</h2>
<p>Again, this plot excludes the experiments where <code>time_block</code> is 300 seconds or more.</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcb_b, <span class="fu">aes</span>(<span class="at">x =</span> abs_block, <span class="at">y =</span> time_block, <span class="at">col =</span> pct_ib, <span class="at">group =</span> pct_ib)) <span class="sc">+</span></span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, <span class="at">formula =</span> y <span class="sc">~</span> x) <span class="sc">+</span></span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Block, by Absolute Block Value&quot;</span>,</span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">glue</span>(<span class="fu">nrow</span>(tcb_b), <span class="st">&quot; experiments which achieved complete block&quot;</span>),</span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Absolute Block Value (mA)&quot;</span>,</span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Block (seconds)&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
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<figure class="figure">
<p><img 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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot1a.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
</section>
<section id="plot-1b.-quadratic-fit" class="level2" data-number="3.3">
<h2 data-number="3.3" class="anchored" data-anchor-id="plot-1b.-quadratic-fit"><span class="header-section-number">3.3</span> Plot 1B. (Quadratic Fit)</h2>
<p>This plot excludes the experiments where <code>time_block</code> is 300 seconds or more.</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcb_b, <span class="fu">aes</span>(<span class="at">x =</span> abs_block, <span class="at">y =</span> time_block)) <span class="sc">+</span></span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">stat_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, </span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>              <span class="at">formula =</span> y <span class="sc">~</span> x <span class="sc">+</span> <span class="fu">I</span>(x<span class="sc">^</span><span class="dv">2</span>), <span class="at">col =</span> <span class="st">&quot;red&quot;</span>) <span class="sc">+</span></span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Block, by Absolute Block Value&quot;</span>,</span>
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">glue</span>(<span class="fu">nrow</span>(tcb_b), <span class="st">&quot; experiments which achieved complete block&quot;</span>),</span>
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Absolute Block Value (mA)&quot;</span>,</span>
<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Block (seconds)&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img 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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot1b.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
<p>The quadratic model shown in plot 1b is summarized below:</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>m1b <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_block <span class="sc">~</span> abs_block <span class="sc">+</span> <span class="fu">I</span>(abs_block<span class="sc">^</span><span class="dv">2</span>), <span class="at">data =</span> tcb_b)</span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>m1bt <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m1b, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m1bt, <span class="at">digits =</span> <span class="dv">3</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: left;">term</th>
<th style="text-align: right;">estimate</th>
<th style="text-align: right;">std.error</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">conf.low</th>
<th style="text-align: right;">conf.high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">(Intercept)</td>
<td style="text-align: right;">212.488</td>
<td style="text-align: right;">35.758</td>
<td style="text-align: right;">0.000</td>
<td style="text-align: right;">139.896</td>
<td style="text-align: right;">285.079</td>
</tr>
<tr class="even">
<td style="text-align: left;">abs_block</td>
<td style="text-align: right;">-158.192</td>
<td style="text-align: right;">56.500</td>
<td style="text-align: right;">0.008</td>
<td style="text-align: right;">-272.893</td>
<td style="text-align: right;">-43.491</td>
</tr>
<tr class="odd">
<td style="text-align: left;">I(abs_block^2)</td>
<td style="text-align: right;">31.792</td>
<td style="text-align: right;">18.600</td>
<td style="text-align: right;">0.096</td>
<td style="text-align: right;">-5.968</td>
<td style="text-align: right;">69.552</td>
</tr>
</tbody>
</table>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m1b) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: right;">r.squared</th>
<th style="text-align: right;">adj.r.squared</th>
<th style="text-align: right;">sigma</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">df</th>
<th style="text-align: right;">df.residual</th>
<th style="text-align: right;">nobs</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: right;">0.4083283</td>
<td style="text-align: right;">0.3745185</td>
<td style="text-align: right;">58.33795</td>
<td style="text-align: right;">0.0001027</td>
<td style="text-align: right;">2</td>
<td style="text-align: right;">35</td>
<td style="text-align: right;">38</td>
</tr>
</tbody>
</table>
</div>
</div>
</section>
<section id="plot-1c.-comparing-the-linear-to-the-quadratic-fit" class="level2" data-number="3.4">
<h2 data-number="3.4" class="anchored" data-anchor-id="plot-1c.-comparing-the-linear-to-the-quadratic-fit"><span class="header-section-number">3.4</span> Plot 1C. Comparing the Linear to the Quadratic Fit</h2>
<p>As we saw above, the <em>p</em> value for adding the quadratic term is about 0.096.</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcb_b, <span class="fu">aes</span>(<span class="at">x =</span> abs_block, <span class="at">y =</span> time_block)) <span class="sc">+</span></span>
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">stat_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, </span>
<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a>              <span class="at">formula =</span> y <span class="sc">~</span> x <span class="sc">+</span> <span class="fu">I</span>(x<span class="sc">^</span><span class="dv">2</span>), <span class="at">col =</span> <span class="st">&quot;red&quot;</span>) <span class="sc">+</span></span>
<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, <span class="at">formula =</span> y <span class="sc">~</span> x, <span class="at">col =</span> <span class="st">&quot;blue&quot;</span>) <span class="sc">+</span></span>
<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Block, by Absolute Block Value&quot;</span>,</span>
<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">glue</span>(<span class="fu">nrow</span>(tcb_b), <span class="st">&quot; experiments which achieved complete block&quot;</span>),</span>
<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Absolute Block Value (mA)&quot;</span>,</span>
<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Block (seconds)&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
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<figure class="figure">
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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot1c.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
</section>
<section id="conclusions" class="level2" data-number="3.5">
<h2 data-number="3.5" class="anchored" data-anchor-id="conclusions"><span class="header-section-number">3.5</span> Conclusions</h2>
<p>Plot 1B shows a slightly more satisfying curve in some regards than does the straight line model shown in Plot 1, but the improvement is associated with a <em>p</em> value of 0.096, so the improvement is not substantial enough, given this sample size, for us to clearly select the more complex quadratic fit.</p>
<p>So I’ll proceed assuming a linear relationship between absolute block value and time to complete block.</p>
</section>
<section id="new-plot-1d.-histogram" class="level2" data-number="3.6">
<h2 data-number="3.6" class="anchored" data-anchor-id="new-plot-1d.-histogram"><span class="header-section-number">3.6</span> NEW! Plot 1D. (Histogram)</h2>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcb, <span class="fu">aes</span>(<span class="at">x =</span> time_block<span class="sc">/</span><span class="dv">60</span>, <span class="at">group =</span> pct_ib)) <span class="sc">+</span></span>
<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_histogram</span>(<span class="fu">aes</span>(<span class="at">fill =</span> pct_ib), <span class="at">col =</span> <span class="st">&quot;white&quot;</span>, <span class="at">binwidth =</span> <span class="dv">1</span>) <span class="sc">+</span></span>
<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_histogram</span>(<span class="at">data =</span> tcb <span class="sc">|&gt;</span> <span class="fu">filter</span>(time_block <span class="sc">==</span> <span class="dv">300</span>), <span class="at">binwidth =</span> <span class="dv">1</span>, <span class="at">fill =</span> <span class="st">&quot;grey30&quot;</span>) <span class="sc">+</span></span>
<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color =</span> <span class="st">&quot;none&quot;</span>, <span class="at">fill =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>( <span class="sc">~</span> pct_ib) <span class="sc">+</span></span>
<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Block, by % of Instantaneous BT&quot;</span>,</span>
<span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="st">&quot;n = 16 runs at each % Instantaneous BT level&quot;</span>,</span>
<span id="cb19-8"><a href="#cb19-8" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption =</span> <span class="st">&quot;Failure to block shown in dark grey, at 5 minutes&quot;</span>,</span>
<span id="cb19-9"><a href="#cb19-9" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Time to Complete Block (minutes)&quot;</span>,</span>
<span id="cb19-10"><a href="#cb19-10" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Number of Experiments&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
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<figure class="figure">
<p><img 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9D11tLTP43JxYNQKC0CoE2oZG+Ez3Vt5tB7v63YUAcBWn4K0bXqK+MT2aUAlZanWo3JauKy+KxPAIrWrCUANGPm5cZ/92yK/qXbUAcBepo8cXd7Vn0qiLkDKgWotLw986aFy/yPAwBFa9YCAProZlIDNGXPUXoAvHnrODF23rxqHoCmO6C3a0ie92TEDtAybvMvbzZh2b9WABQduoIDNENlDdB0Q0z34DKdlYeIc2kegJ4XVReDpytjfjzdgQA0n4J6RQAoWqdCAzQ7ZD/6i93mV+zB7UN7AGjfDujhALS1IgAUrVN+AZpxZfPWU0ly4cXWVnDeOk3WRGS65V17UG9+ZwMH7tnO3P30oPHCd2p0FcuakY9eOe69jP9e/v3i6vFZz2nW3Hd09W7f8rufl8MOr2gN0N4d0Ao19rKb5bXK6Niky/WtXuvr7qwOzWJ7D7RevRGANrNOqxannvzH7cIAKIpJ3gH6OzeLjfpiE4QWgP7gbnPzyzafj9/IQPVOa+B0iReK6xY9AG1H3qtiWhXkH5Fef78HoGfVz6/1LN/4+bVyRa/eKL5ube/Nc6BnvbuaNUAtZdfldcro2KTLmau3+/rom+X6tAE6MIv1OdD3bjbX3A7QVvZ59QuQztV1szAAimKSd4CmW+SD7aM7bTgNAzTXbvPL/uP82IRbcaT/ne2j14xNvRuZbpaXMia/mbQ36XQ7vfaguGKVf7+7oaY/z3aCPn6j3JK7y6dfp+NmnLpereiL5ooW53Dz87ePb/SexK0Bai27wZ5mGcYES5frWb2L6d+oFIN5O7qzOjSLzWvuVxvzZwVoO3t37rQ4y90tDICimOQfoDkeq8MzmZoAvXBcbaAdMJXc7QNoM3J3EuC0dTZgt9mm/5FXdmrsOu7OImTbdXf5ypYRsie1OU5Ze/8OaBOgtrLL8nrKaOcKl+tZvaLsen1aszowiy2AXrxbf98G0G726W4m03+7PwSgKCp5B+il6gGVCXchNQFa7Bk9eiVpDVBu59tegLYiB67en1ffrkbqbKj1ydeimO7yuwPP8zaAjBXNKs+eRMp3QDdvHCdH11pAqAFqLbssr1uGkStcrrt6dVxxIG0CtP8eiNZdn41FbAAdmNribEf3hwAURSXvAB25GtSrFkDL7eesdahf7+j1ALQVeZ5dkGifQG3rtA+gTQB0NuEugYQrmt1DXz4V0DqNWAPUWrbBkdPetRUuZ6xejf1iye6sDs1iHffeK83TvxaAGtnl1af+wgAoikr+r8IX/9XmyoRzoNVPW7cZNja0vqvwrcjiasqFF983y/v4vbdffSrpI8/jG80Cq9Iby3e266EV3Sl/iDNd7VuNg9TmmoyV3cxrlGHYZMt1V69x4qH4T+kstm9jqndBLQA1p/a0RPblvnkHoCgmLQ6gnSO67hLjAC1eRpLt+LVvwbn3VIuPowBtLz8VoOUO6GVjiSGAdsre5bXLGAaodbkegBp7haJZbE7DWeM09SSAnif5H5fyVAgARRFrWQBNN59qr8YdoKnee/WpTlBxNH3lhT97v/cQvr2/27P8RIA+PK6pdN46nzgI0HbZ9ZWWVhlDAB1Zrrt643ug/bPYmoZzMUA7P8pPD59WF7su93gQikT7AeiI6s1vd62mukvQWKKOGEBK5XizdYtmeZvPzmaeAzXuamot37z9u+ecoblCt7fTAdoou74DqFXGEEBHljNWb/gc6KltFl32QHtuyEh3z8tA84cAFMWkhQH04e4dIqfdHcgaoK3DfRMV1ZbcREHrIlTf1ZfWru+lB8byzdu/b4+u6Hm1eyUFqFH27mn6dhkDAB1drrN6jUt0xX5kZ1YHZrH7JJLoHKiRnU/Pvd3b8Do/BKAoJi0MoBk3j14rbgbqv0q+Y8V5/1nBs8YNPa2rxPXV/T6AZneOF1/nqDSW390XZN/v3aXdLpYVnwPtlt0F45n1HOjocp3VM+4D7c5q/yy2rsLfFF6FN7Kz9Cf+Sbm88UMAimLS0gDauKpwu3+J8pGg7CVOfajIdoxuZc/k3GmNUB7jZpt9wYYMEZv3Wwtk10uqY1Zj+fPuk0iWFT0vi51wFb5bdlletwwjV75ca/WaTyJVTyp1ZrVvFtv3gUqfhe9mF8Pc7p13AIqi0tIAWrwdNCneCtq7RHlzZXLpzf59soe9jzI9LodNrr5ZbLv5da3mRr97Jrt4tKe7/O7n1bPwwyu6e4izLLVFhKFzoJ2yy/K6ZRg26XKd1au/Lp6F787qwCy2AFo6jQ42ly7CutnVfm7PvANQFJUWB9Bt/hKhxluDjCWKtwzdGrwKv3nrSro5XrjWvoWxHPad3em+eykiLje31UevZBedq/cPGcsbb2MaXNH6Ic5sj27wSSR72WV5nTJMm3S5zupVL0HazVJ7VodmsfmZSM2f2AFqZNflGT8EoCgmhX4fKEIIRSsAihBCjgKgCCHkKACKEEKOAqAIIeQoAIoQQo4CoAgh5CgAihBCjgKgCCHkKACKEEKOAqAIIeQoAIoQQo4CoAgh5CgAihBCjgKgCCHkKACKEEKOAqAIIeQoAIoQQo4CoAgh5CivAP0ttEzRpRjExhSF5gTo36MFqgvQ0PWgPhkADV0Q6hMAXZ8AaAwCoFEIgK5PADQGAdAoBEDXJwAagwBoFAKg6xMAjUEANAoB0PUJgMYgABqFAOj6BEBjEACNQgB0fQKgMQiARiEAuj4B0BgEQKMQAF2fAGgMAqBRCICuTwA0BgHQKARA1ycAGoMAaBQCoOsTAI1BADQKAdD1CYDGIAAahQDo+gRAYxAAjUIAdH0CoDEIgEYhADqqn3/xH/4f+X/83T//4pNP/uO/DFyOXgcH0L/779PG/Nv/afnFYXbpANr0N//xk3VnDrVNALSrv/tnTxYA/dvfezLTv/U/hq5Iq0MD6N8UjXny38++ONQuxd+mn38x78y/+V9lXxxsmwBoV3/9ZAnQ7z75D//y7/+mwmnEOjCApn/hfuMv//7v/odi0zzULh1CmxqdOdg2AdCOsr+beZd//sX8z+Xf/l7xJzRiHRhAf1bux/z1k795wF2Kvk2tzhxumwBoW+nfzf+kOAeab5/5v78btCK9DgugaYf+s/qrg+1S7G1K/87lnSnadbhtAqBtfffJ3ywvIn233E7LX4SIdVgA/dvfa55IO9guxd6m9k7n4bYJgLb0s/TwvQDo3/2z8mhjd1E+Wh0WQLN+/G//6Mknf+O//PtD7lLsbarPgf7mQbcJgDaV/7kEoItW2o9/XlyF/91D7lLsbcpuNivulTjojQmAtvTdbKM0ABr7vReHBdCfFRtlunmmDTrcLsXeprQl/1EO0N/4y0PemABoU3+dX39nD3TR+tmT5YWI76YHh4fbpdjblN3O0vd37tDaBEBr/fyLeZsB6KJVdqlozOF2KfY2FUdzf3/wf+cAaK2/fnKn9EDjYC8cRt6l3VFg/h8H26XY29Rm5uG2CYDWagO0umXt4G5di7xLuy0zu2PicLt0MG3KAXq4bQKghsrDjIN9eCL2Ln233Ispr/gdaJcOoE2/W/570A+MAVBDJUCLR64P8PHd2Lv08y9mb/Uprk4cbpeib9PPGjdLHHCbAKih6kT33xzoC2Si79LPytf85KfVDrVL8bepOiP2u9kXB9smAGpod6Xwb7Ibtv9x7H8yDw+gaWNShP57f1l9cZBdOoA2/e/Z+0APvU0A9PB1cAA9SB0eQA9SAHR9AqAxCIBGIQC6PgHQGARAoxAAXZ8AaAwCoFEIgK5PADQGAdAoBEDXJwAagwBoFAKg6xMAjUEANAoB0PUJgMYgABqFAOj6BEBjEACNQgB0fQKgMQiARiEAuj4B0BgEQKMQAF2fAGgMAqBRaF6AomWKLsUgNqYoBEDXJ7oUg9iYotCcAJ24/IcfquJUbl12wOjp2V2ATg5cz1QFzDYAOjVvPVPlMVu7MQHQ6KIB6GFmA9AQ2QDU2bweKgDQGLIBaIhsAOpsXg8VAGgM2QA0RDYAdTavhwoANIZsABoiG4A6m9dDBQAaQzYADZENQJ3N66ECAI0hG4CGyAagzub1UAGAxpANQENkA1Bn83qoAEBjyAagIbIBqLN5PVQAoDFkA9AQ2QDU2bweKgDQGLIBaIhsAOpsXg8VAGgM2QA0RDYAdTavhwoANIZsABoiG4A6m9dDBQAaQzYADZENQJ3N66ECAI0hG4CGyAagzub1UAGAxpANQENkA1Bn83qoAEBjyAagIbIBqLN5PVQAoDFkA9AQ2QDU2bweKgDQGLIBaIhsAOpsXg8VAGgM2QA0RDYAdTavhwoANIZsABoiG4A6m9dDBQAaQzYADZENQJ3N66ECAI0hG4CGyA4D0E//8OTk5Fs/MQefOM6K5t1fNAA9zGwAGiI7CEA/eekk07PfNwafONCK5t1fNAA9zGwAGiI7BEA/f/3kKz/Zfpr+76+7g08caUXz7i8agB5mNgANkR0CoJ+89KWfpv989rKxC7qenkf16wZAY8gGoCGyQwD0o5OvZv+kO6J/3B184kgrmnd/0QD0MLMBaIhs9kCdzeuhAgCNIRuAhsgOeg70q/WgpT5EHjS9JS0Z43UBGmCV0KgMgBpL+P69QA7ycRX+8x/nV+H/YHcNCYD61PZEJQAap0YB6rClthRipQ5PXm5j+q/zLfXLxo2g6znqmDVaCVBDHMLHkC04hP8PFfJdbtscbZvCnAPNdj7T3VDOgc4TDUA9RUeVDUBDZIcA6Lsn3y7//WrnJwDUSzQA9RQdVTYADZEdAKCfv17ueX7yUvdOegDqJRqAeoqOKhuAhsgGoM7m5VIBgHqKjiobgIbI5hDe2bxcKgBQT9FRZQPQENkhAPrRCReRAKjcrTJH+xsCQCfYw2WHAOj2g3JT/bYx+MSBVjTvU6IBqKfoqLIBaIjsIADd/r/Z+0D/Ke8DnSkagHqKjiobgIbIDgPQwcEnLr+ieZ8SDUA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QDUGfzcqkAQD1FR5UNQENkA1Bn83KpAEA9RUeVDUBDZANQZ/NyqQBAPUVHlQ1AQ2QvDKAfIr20ADUG7AI0wDqhURkA7S6gBWiIlTo8sQfqJZs9ULlbZY72N4Q90An2cNkL2wOduPyK5n1KNAD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9eR1OzYAACAASURBVBQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RDUCdzculAgD1FB1VNgANkQ1Anc3LpQIA9RQdVTYADZENQJ3Ny6UCAPUUHVU2AA2RHQagn//4pZOT/+CPzMEnjrOieZ8SDUA9RUeVDUBDZAcB6KcvF5vqHxiDTxxoRfM+JRqAeoqOKhuAhsgOAdDPXz/58k+2n/+fJ89+vzv4xJFWNO9TogGop+iosgFoiOwQAP3o5Es/zf794OSr3cEnjrSieZ8SDUA9RUeVDUBDZAcAaLoD+sdDg08cakXzPiUagHqKjiobgIbIDgDQz14udkD7Bp841IrmfUo0APUUHVU2AA2RHQCgn7z0lV//P//FycmX/6fGoKU+RHppAWoM2AWo94JV8l1NrDIA2l1AC9AQK3V48gLQ7xWb6rd3vQagHhUbQCf/BrXluZpoBUCjkB6gH6Ub6R/8evv5j7kKP1O0EqCGZj+E/wfu0kaP2FVmDuGlirdNAQ7hP6p2Pd/lKvw80QBUHj1iV5kBqFTxtinIOdByzzM7GdoZfOJQK5r3KdEAVB49YleZAahU8bZpboBufvn2Dzvf+uSl8ir87j/qwScWs6J5nxINQOXRI3aVGYBKFW+b5gPoo//mwXb7+GaSJBe/0/rB56+Xe6AfnbAHOks0AJVHj9hVZgAqVbxtmg2gZ8kTP9puT5NM2X81VJ37fLe+DF8NPrGYFc37lGgAKo8esavMAFSqeNs0F0DPc2w+PE4uPXh0I7ne+tknL5186ydchZ8vGoDKo0fsKjMAlSreNs0F0NOUnBlGj76T/W/23w199FK+pT5rPNEJQL1EA1B59IhdZQagUsXbppkAurmTkbPE6OMbnWP47affSxH6T39iDj6xmBXN+5RoACqPHrGrzABUqnjbNBNAC2Y+vpFc3vYBdHDwicWsaN6nRANQefSIXWUGoFLF26ZZAfrwOLm9BaB7jwag8ugRu8oMQKWKt02zHsKf5adAzXOgw4NPLGZF8z4lGoDKo0fsKjMAlSreNs13Eelydvk9I6dxFd4y+MRiVjTvU6IBqDx6xK4yA1Cp4m3TjLcxZbq93bySFPuhosEnFrOieZ8SDUDl0SN2lRmAShVvm+YCaHr4nupyfiHp6LZ48InFrGjep0QDUHn0iF1lBqBSxdum2QC6ffTq119L/3n8tat35YNPLGZF8z4lGoDKo0fsKjMAlSreNs0HUBcBUC/RAFQePWJXmQGoVPG2aa6r8K9+vb7w/vD53+Yq/D6jAag8esSuMgNQqeJt06z3gfZ9YR98YjErmvcp0QBUHj1iV5kBqFTxtmkfAH14DED3Gg1A5dEjdpUZgEoVb5v8A/TxjcQQN9LvNRqAyqNH7CozAJUq3jbNsAd6bgJUeh8TAPUSDUDl0SN2lRmAShVvm2YA6OZ/fuGF54+Pnnmh0jfeEQ8+sZgVzfuUaAAqjx6xq8wAVKp427SPc6ATBp+4/IrmfUo0AJVHj9hVZgAqVbxt2sdtTBMGn7j8iuZ9SjQAlUeP2FVmACpVvG2aCaCOAqBeogGoPHrErjIDUKnibdOsAP1Vpfelg08sZkXzPiUagMqjR+wqMwCVKt42zQbQR680rsJzH+heowGoPHrErjIDUKnibdNcAG3fDQpA9xoNQOXRI3aVGYBKFW+b5gLoWZJc/MbblX7IjfT7jAag8ugRu8oMQKWKt01zXYW/k3+c3OTBJy6/onmfEg1A5dEjdpUZgEoVb5tmuw9U/Bb61uATl1/RvE+JBqDy6BG7ygxApYq3TbMBlBvpw0UDUHn0iF1lBqBSxdum2Q7h2QMNFw1A5dEjdpUZgEoVb5vmu4gk/STO1uATl1/RvE+JBqDy6BG7ygxApYq3TXMBNN0FvTW9GgDqJRqAyqNH7CozAJUq3jbNdQj/6vNJUr+QSfpgPAD1Eg1A5dEjdpUZgEoVb5tmu4iUcCN9sGgAKo8esavMAFSqeNsEQJ3NANRXvQDURzYADZE91zlQNwFQL9EAVB49YleZAahU8bYJgDqbAaivegGoj2wAGiJ7XoBupO+xqwafWMyK5n1KNACVR4/YVWYAKlW8bZoRoPefy05+Pv7ai/J30wNQL9EAVB49YleZAahU8bZpNoBu3iiuHj2+kVwUP9UJQL1EA1B59IhdZQagUsXbptkAepokF3//+Ikfbf5E/rHwANRPNACVR4/YVWYAKlW8bZoLoOdJcqt8pci9Yz4Xfr/RAFQePWJXmQGoVPG2aS6AnmbPwpfvZDoTvxsUgHqJBqDy6BG7ygxApYq3TTMBtHgbUwnQh8fcSL/XaAAqjx6xq8wAVKp42zQTQAt0lgCVvxwUgHqJBqDy6BG7ygxApYq3TQDU2QxAfdULQH1kA9AQ2bMdwmcXjkpynosvwwNQL9EAVB49YleZAahU8bZprotI+YWjAqATPmAOgHqJBqDy6BG7ygxApYq3TXMB9OFxcu1BDtBHNxPxx3sAUC/RAFQePWJXmQGoVPG2aS6AZh8Mn1w5PnrmqfRf8ad7AFAv0QBUHj1iV5kBqFTxtmk2gG5/cVy9DVT+6UgA1Es0AJVHj9hVZgAqVbxtmg+g24/fupLS88LVuxMGn1jMiuZ9SjQAlUeP2FVmACpVvG2aEaAOAqBeogGoPHrErjIDUKnibRMAdTYDUF/1AlAf2QA0RPaMAP3l2zv9kPtA9xkNQOXRI3aVGYBKFW+bZgNofQ2JD5XbdzQAlUeP2FVmACpVvG2aC6DnfCpnuGgAKo8esavMAFSqeNs0E0A3d5Kj1361k/SjkQCol2gAKo8esavMAFSqeNs0E0Af3xC/RLk1+MTlVzTvU6IBqDx6xK4yA1Cp4m3TbACVHrW3B5+4/IrmfUo0AJVHj9hVZgAqVbxtmu0QHoCGiwag8ugRu8oMQKWKt01zXUQ64xA+XDQAlUeP2FVmACpVvG2aC6CPb4hfwdQcfOLyK5r3KdEAVB49YleZAahU8bZptvtAH91ILr5Q6evcSL/PaAAqjx6xq8wAVKp42zQbQN/gPtBg0QBUHj1iV5kBqFTxtmm+c6AANFg0AJVHj9hVZgAqVbxtmu0qfHL0mvC4vTn4xOVXNO9TogGoPHrErjIDUKnibdNs94G6XEMCoH6iAag8esSuMgNQqeJt02wA5T7QcNEAVB49YleZAahU8bZptkN4ABouGoDKo0fsKjMAlSreNs13EUn+SUiNwScuv6J5nxINQOXRI3aVGYBKFW+b5gLo5s7RrenVAFAv0QBUHj1iV5kBqFTxtmmuQ/hXn0+So2cm30j/IdJLC1BjwC5AfderA6jnaqKVAdDuAlqAhlipw5P4dXbcBxosmj1QefSIXWVmD1SqeNs021V4ABouGoDKo0fsKjMAlSreNs11DtRNANRLNACVR4/YVWYAKlW8bQKgzmYA6qteAOojG4CGyAagzmYA6qteAOojG4CGyPYO0M2r2TX39H+b4nV2e40GoPLoEbvKDEClirdN3gH6+EZ2yYiLSCGjAag8esSuMgNQqeJtEwB1NgNQX/UCUB/ZADRENudAnc0A1Fe9ANRHNgANkQ1Anc0A1Fe9ANRHNgANkT0XQE+v3nWoBoB6iQag8ugRu8oMQKWKt00zAfTxDT7WOFw0AJVHj9hVZgAqVbxtmg2gvA80XDQAlUeP2FVmACpVvG2aCaCbO3ykR7hoACqPHrGrzABUqnjbNNc50LPkksNJUADqJRqAyqNH7CozAJUq3jbNBdCP30ySC9PfBzqxmBXN+5RoACqPHrGrzABUqnjbNN9FJG6kDxYNQOXRI3aVGYBKFW+bAKizGYD6qheA+sgGoCGy5zqEdxMA9RINQOXRI3aVGYBKFW+bAKizGYD6qheA+sgGoCGy5wXo5v2Jg08sZkXzPiUagMqjR+wqMwCVKt42zQjQ+89lJz8ff+1F4SX4LQD1FA1A5dEjdpUZgEoVb5tmA+jmjeLq0eMbyUXxQ0kA1Es0AJVHj9hVZgAqVbxtmg2gp0ly8fePn/jR5k+S5JJ0HxSAeokGoPLoEbvKDEClirdNcwH0PElulU/E3zsWv1gEgHqJBqDy6BG7ygxApYq3TXMB9DS5vnulyFlyWTr4xGJWNO9TogGoPHrErjIDUKnibdNMAC1eJlIC9OExN9LvNRqAyqNH7CozAJUq3jbNBNACnSVA5e+2A6BeogGoPHrErjIDUKnibRMAdTYDUF/1AlAf2QA0RPZsh/DZhaOSnOfiy/AA1Es0AJVHj9hVZgAqVbxtmusiUn7hqABoClMuIu01GoDKo0fsKjMAlSreNs0F0IfHybUHOUAf3UzEb6cHoF6iAag8esSuMgNQqeJt01wATXdBk+TK8dEzT6X/XhcPPrGYFc37lGgAKo8esavMAFSqeNs0G0C3vziu3gYq5icA9RMNQOXRI3aVGYBKFW+b5gPo9uO3rqT0vDDlA+IBqJdoACqPHrGrzABUqnjbNCNAHQRAvUQDUHn0iF1lBqBSxdummQH6q4mDTyxmRfM+JRqAyqNH7CozAJUq3jbNCNDsfaBJcsQh/N6jAag8esSuMgNQqeJt02wA3dzZfaTcNfEblQGol2gAKo8esavMAFSqeNs0F0Azfh498+dv/+D540R8Hz0A9RMNQOXRI3aVGYBKFW+b5gLoWZJ8odjx3LyZ8D7Q/UYDUHn0iF1lBqBSxdummQCa7oDWd3+e8ijnfqMBqDx6xK4yA1Cp4m3TTAB9fKPx+CbvA91zNACVR4/YVWYAKlW8bZoNoA1m8jq7PUcDUHn0iF1lBqBSxdum2Q7hW3ugvM5ur9EAVB49YleZAahU8bZpvotIl3v/e2TwicWsaN6nRANQefSIXWUGoFLF26YZb2Oqbv88S6RH8ADUTzQAlUeP2FVmACpVvG2a6xD+1ez+z2defPuvsn+ffiHX10cP5AGol2gAKo8esavMAFSqeNs020WkxNT4jigA9RINQOXRI3aVGYBKFW+bAKizGYD6qheA+sgGoCGy5zoH6iYA6iUagMqjR+wqMwCVKt42AVBnMwD1VS8A9ZENQENkA1BnMwD1VS8A9ZENQENkz3UO9L9tXnG//+/yJNI+owGoPHrErjIDUKnibdNsF5Eu7p5E2rwhvhEUgHqJBqDy6BG7ygxApYq3TTNehS9fx/TopuT6ezn4xGJWNO9TogGoPHrErjIDUKnibdNc50DvHSfJxezDPLJPN74k/VQPAOolGoDKo0fsKjMAlSreNs12ESnb8Ty6lb+Y/pvywScWs6J5nxINQOXRI3aVGYBKFW+b5rsKv3mjuH3+Ih8qt+9oACqPHrGrzABUqnjbNOdtTPePs0+UmzT4xGJWNO9TogGoPHrErjIDUKnibdOMAH30Sv6xxvIDeADqKRqAyqNH7CozAJUq3jbNB9D8MtJb2SUk4duUtwDUUzQAlUeP2FVmACpVvG2aC6CbbPfzCw/Ka0niwScWs6J5nxINQOXRI3aVGYBKFW+bZrwPtLiVPr+WdE24EwpAvUQDUHn0iF1lBqBSxdum+QC6g+b9Y26k3280AJVHj9hVZgAqVbxtmgugX2sctm/uANC9RgNQefSIXWUGoFLF26a5zoG+3/rq/wKg+4wGoPLoEbvKDEClirdNc94HOl0A1Es0AJVHj9hVZgAqVbxt8g7QzS/f/mH7ktHj5648zR7oPqMBqDx6xK4yA1Cp4m2Td4A+vlFcMtq8Wn0KZ/UdyeATi1nRvE+JBqDy6BG7ygxApYq3TbMBtMbmAEA/eekrvzYGn1jMiuZ9SjQAlUeP2FVmACpVvG0KBtDPXz8BoDNFA1B59IhdZQagUsXbpmAA/eAEgM4VDUDl0SN2lRmAShVvm0IB9JOXAOhs0QBUHj1iV5kBqFTxtikQQNMD+H/BOdC5ogGoPHrErjIDUKnibVMggL578lUuIs0WDUDl0SN2lRmAShVvm8IA9KP08L0J0N+q9CHSSwtQY8AuQH3XqwOo52qilQHQ7gLzAnRMIaZkifIB0M9efvb7WwA6lwDoKgVAo5APgL578m3uA50xWglQQxzCu5kP7BB+RvzG26YQh/Af5NffAehs0QBUHj1iV5kBKAA1pAfoJy+lB/AAdMZoACqPHrGrzAAUgBoSANRUC6Af1Nvql37aGXxiMSua9ynRAFQePWJXmQEoADUEQL1kA9CmAKiHbADqaF4WQDevvmDq6z2ficQh/GzRAFQePWJXmQEoADXk7YXKAHS2aAAqjx6xq8wAFIAaAqBesgFoUwDUQzYAdTQDUFfFNO9TogGoPHrErjIDUABqiM9E8pINQJsCoB6yAaijGYC6KqZ5nxINQOXRI3aVGYACUEMA1Es2AG0KgHrIBqCOZgDqqpjmfUo0AJVHj9hVZgAKQA0BUC/ZALQpAOohG4A6mgGoq2Ka9ynRAFQePWJXmQEoADU0+iTSnaPvpP98/L5LNQDUSzQAlUeP2FVmAApADY0/C589+F7872QBUC/RAFQePWJXmQEoADU0DtBsDxSAhowGoPLoEbvKDEABqKHxQ/jk0g9/9d6NJ975VS3p8TwA9RINQOXRI3aVGYACUEOjF5HOxl5nZxt8YjErmvcp0QBUHj1iV5kBKAA1NArQzRsANHA0AJVHj9hVZgAKQA0JbmPa/Ortvzo++vO3a/2w532gvYNPLGZF8z4lGoDKo0fsKjMABaCGZPeBchEpZDQAlUeP2FVmAApADckAunm17yX044NPXH5F8z4lGoDKo0fsKjMABaCGeBLJSzYAbQqAesgGoI7mpQL047euJMnRlRcnPJMEQL1EA1B59IhdZQagANSQGKD17UzX5YNPLGZF8z4lGoDKo0fsKjMABaCGpADN+HnhmReef2oKQQGol2gAKo8esavMABSAGhIC9OFxculu/l+P7iT560VEg08sZkXzPiUagMqjR+wqMwAFoIaEAD1NLlWX4Td3ksvSwScWs6J5nxINQOXRI3aVGYACUEPC25juNPY6Hx7vYDo2+MRiVjTvU6IBqDx6xK4yA1AAasjhRnr5XfUA1Es0AJVHj9hVZgAKQA0BUC/ZALQpAOohG4A6mpcI0M2d5Pbui/OEQ/i9RgNQefSIXWUGoADUEBeRvGQD0KYAqIdsAOpoXiRAHx4nF9/J/+u9m9zGtOdoACqPHrGrzAAUgBqaciN9cuXKlUmPIgFQL9EAVB49YleZASgANSR+lPPecfkk59Et+eATi1nRvE+JBqDy6BG7ygxAAagh+ctENvefT/dAn3ltwnvtAKiXaAAqjx6xq8wAFIAa4nV2XrIBaFMA1EM2AHU0A1BXxTTvU6IBqDx6xK4yA1AAagiAeskGoE0BUA/ZANTRDEBdFdO8T4kGoPLoEbvKDEABqCEA6iUbgDYFQD1kA1BHMwB1VUzzPiUagMqjR+wqMwAFoIYAqJdsANoUAPWQDUAdzQDUVTHN+5RoACqPHrGrzAAUgBoCoF6yAWhTANRDNgB1NC8SoKdX7zpUA0C9RANQefSIXWUGoADUkPSFyo33gU4YfOLyK5r3KdEAVB49YleZASgANeTwRvoJg09cfkXzPiUagMqjR+wqMwAFoIYcPlRuwuATl1/RvE+JBqDy6BG7ygxAAagh4TnQs+pj4acNPnH5Fc37lGgAKo8esavMABSAGhIC9OM3k+TCMy+U+jqfibTPaAAqjx6xq8wAFIAaEl9EaopP5dxrNACVR4/YVWYACkANAVAv2QC0KQDqIRuAOpqXCFBHAVAv0QBUHj1iV5kBKAA1BEC9ZAPQpgCoh2wA6mheMEA3708cfGIxK5r3KdEAVB49YleZASgANSQH6P3nspOfj7/2ovxT5QCol2gAKo8esavMABSAGpICdPNGcfXo8Y3kovihJADqJRqAyqNH7CozAAWghqQAPU2Si79//MSPNn+SJJek+6AA1Es0AJVHj9hVZgAKQA0JAXqeJLfKJ+LvHYtfLAJAvUQDUHn0iF1lBqAA1JD0dXbJ9d0rRc6Sy9LBJxazonmfEg1A5dEjdpUZgAJQQ1NeJlIC9OExN9LvNRqAyqNH7CozAAWghqa8zq4EqPzddgDUSzQAlUeP2FVmAApADQFQL9kAtCkA6iEbgDqalwjQzZ3swlFJznPxZXgA6iUagMqjR+wqMwAFoIbE7wO9XAE0hSkXkfYaDUDl0SN2lRmAAlBDQoA+PE6uPcgB+uhmIn47PQD1Eg1A5dEjdpUZgAJQQ9Ib6c+SJLlyfPTMU+m/18WDTyxmRfM+JRqAyqNH7CozAAWghsTPwv/iuHobqJifANRPNACVR4/YVWYACkANyV8m8vFbV1J6XpjyAfEA1Es0AJVHj9hVZgAKQA3xPlAv2QC0KQDqIRuAOpoXDNDNryYOPrGYFc37lGgAKo8esavMABSAGpr2PtBUHMLvPRqAyqNH7CozAAWghsTvA72z+0i5a+I3KscEUJ1WBdBRAVAP2TEDVKkDBGjGz6Nn/uztHzyfElR6H/32tz5ckKbOTFfhClcC1BiwC1AzUSkVQPcxpTHIAGh3gZgBGmJC55H8faDl3UubN5NY3wf6/ymkjLabl7cHqkDgP2AP1Ev2wvdAdW6/U9U2L3EPNN0Bre/+PI31UU4AWgiAupkBqDe336lqm5cI0Mc3Go9vRvs+UABaCIC6mQGoN7ffqWqblwnQBjOjfZ0dAC0EQN3MANSb2+9Utc1LBGjxRvpSD48jfZ0dAC0EQN3MANSb2+9Utc1LBGjrc5DOxE/DA1BZ3QAUgBoCoG5aJkAf30i+UO52niXSI3gAKqwbgAJQQwDUTQsD6ObVF3I9X9wH+lcvHCfJM1/nEH5itN0MQAGoIQDqpoUBNN31NMVFpKnRdjMABaCGAKibAKizACgAHbGrzADUm9vvVLXNywKoSgBUVjcABaCGAKibAKizACgAHbGrzADUm9vvVLXNANRVABSAjthVZgDqze13qtrmpQL0l2/v9EOuwk+MtpsBKAA1BEDdtFCA1p8px0Ukh2i7GYACUEMA1E3LBOg5V+FV0XYzAAWghgComxYJ0M2d5Oi1X+30vnTwicUA0F4BUPFUjdlVZgDqze13qtrmJQL08Q3xS5Rbg09cHoD2CoCKp2rMrjIDUG9uv1PVNi8ToOLn31uDT1wegPYKgIqnasyuMgNQb26/U9U2LxGgmzsAVBVtNwNQAGoIgLppkQDdnnEIr4q2mwEoADUEQN20TIC2PtNDPvjE5QForwCoeKrG7CozAPXm9jtVbfMiAbp9dCO5+EIlXmc3NdpuBqAA1BAAddNCAfoG94Fqou1mAApADQFQNy0ToGfcSK+KtpsBKAA1BEDdtEiA5jfSC4/bm4NPXB6A9gqAiqdqzK4yA1Bvbr9T1TYvEaBu15AAqLBuAApADQFQNy0UoNwHqoq2mwEoADUEQN20SIByI70y2m4GoADUEAB10yIBOuGz4FuDT1wegPYKgIqnasyuMgNQb26/U9U2LxKgmztHt6ZXA0BldQNQAGoIgLppkQDdvJp/Ljw30rtG280AFIAaAqBuWiRAOx9uzH2gU6PtZgAKQA0BUDcBUGcBUAA6YleZAag3t9+papuXCFBHAVBZ3QAUgBoCoG4CoM4CoAB0xK4yA1Bvbr9T1TYDUFcBUAA6YleZAag3t9+papsXCdCPf9UUHyo3NdpuBqAA1BAAddMiAcpFJGW03QxAAaghAOomAOosAApAR+wqMwD15vY7VW3zEgG6+eXbpf70ZnL05z/kRvqJ0XYzAAWghgComxYJ0KYeHl+SvhkUgMrqBqAA1BAAmEUtcAAAIABJREFUddPiATrhxSIAVFY3AAWghgCom5YPUPkuKACV1Q1AAaghAOqm5QNU/nZlACqrG4ACUEMA1E3LB+jDYwA6NdpuBqAA1BAAddPiAbo5TTiEnxptNwNQAGoIgLppkQDdvFq9CvSF548TLiJNjrabASgANQRA3bRIgLZvpOc2psnRdjMABaCGAKibFg/QCy9K+QlAhXUDUABqCIC6aZEAdRQAldUNQAGoIQDqJgDqLAAKQEfsKjMA9eb2O1VtMwB1FQAFoCN2lRmAenP7naq2GYC6CoAC0BG7ygxAvbn9TlXbvCyANm5gqsXHGk+NtpsBKAA1BEDdtDCAdt4EyvtA3aLtZgAKQA0BUDcBUGcBUAA6YleZAag3t9+papuXBdCu7h8nPIk0PdpuBqAA1BAAddOiAbp5JcXnxbviwScWA0B7BUDFUzVmV5kBqDe336lqmxcM0Hvp7ueR/EEkACqsG4ACUEMA1E3LBejE3c8tAJXWDUABqCEA6qbFAjTf/fzmtMEnFgNAewVAxVM1ZleZAag3t9+papuXCdBHk3c/twBUWjcABaCGAKiblglQh93PLQCV1g1AAaghAOqmJQL00Z3sLaATdz+3AFRaNwAFoIYAqJsWCNB89/OWQzUAVFY3AAWghgComxYHUNfdzy0AldYNQAGoIQDqpqUB9Beuu59bACqtG4ACUEMA1E0LAyjPwgNQABoiG4C6CYA6C4AC0BG7ygxAvbn9TlXbvCyAit4H+q//8OTk2W/9xBx8YjEAtFcAVDxVY3aVGYB6c/udqrZ5WQCV6MfFlvrs943BJw4EQHsFQMVTNWZXmQGoN7ffqWqbowPoRyfP/tF2++nrJ1/6aXfwiSMB0F4BUPFUjdlVZgDqze13qtrm2AD6+esnf5z9+9nLxb/NwScOBUB7BUDFUzVmV5kBqDe336lqm2MD6Gcvl3ue7558uzv4xKEAaK8AqHiqxuwqMwD15vY7VW1zbADdCYC61g1AAaghAOqmaAH62cu7q0i/VelDv3KurZAOoOEKVwLUqKYL0J5ylwrQUWm6pP398lu5AVCz2pAI1LlVbVqU/AH0g5Ov7no9D0CdSysVDKDKugGoXIouLaxNADQKeQPoR/u4jUmHQJ3bXvmchSu3TEPLPoTXRevapMr23CYO4d0U6SH8Ry89270GD0A9FQ5A5W5dm1TZAFTu1rXJbo4SoB/07H8CUE+FA1C5W9cmVTYAlbt1bbKbYwToj3v5CUD9FA5A5W5dm1TZAFTu1rXJbo4PoJ+/e/Ll7kNIxeATBwKgvdEAVO7WtUmVDUDlbl2b7Ob4APruyVd+3T/4xIEAaG80AJW7dW1SZQNQuVvXJrs5OoB+MMRPAOqncAAqd+vapMoGoHK3rk12c2wA/ezl3S/BVzs/AqBeCgegcreuTapsACp369pkN8cG0I/qXwIAOkvhAFTu1rVJlQ1A5W5dm+zm2ABqG3zi8gC0NxqAyt26NqmyAajcrWuT3QxAh6VDoM5tr3zOwgGo3K1rkyobgMrdujbZzQB0WDoE6tz2yucsHIDK3bo2qbIBqNyta5PdDECHpUOgzm2vfM7CAajcrWuTKhuAyt26NtnNAHRYOgTq3PbK5ywcgMrdujapsgGo3K1rk90MQIelQ6DOba98zsIBqNyta5MqG4DK3bo22c0AdFg6BOrc9srnLByAyt26NqmyAajcrWuT3QxAh6VDoM5tr3zOwgGo3K1rkyobgMrdujbZzQB0WDoE6tz2yucsHIDK3bo2qbIBqNyta5PdDECHpUOgzm2vfM7CAajcrWuTKhuAyt26NtnNAHRYOgTq3PbK5ywcgMrdujapsgGo3K1rk90MQIelQ6DOba98zsIBqNyta5MqG4DK3bo22c0AdFg6BOrc9srnLByAyt26NqmyAajcrWuT3QxAh6VDoM5tr3zOwgGo3K1rkyobgMrdujbZzQB0WDoE6tz2yucsHIDK3bo2qbIBqNyta5PdDECHpUOgzm2vfM7CAajcrWuTKhuAyt26NtnNAHRYOgTq3PbK5ywcgMrdujapsgGo3K1rk90MQIelQ6DOba98zsIBqNyta5MqG4DK3bo22c0AdFg6BOrc9srnLByAyt26NqmyAajcrWuT3QxAh6VDoM5tr3zOwgGo3K1rkyobgMrdujbZzQB0WDoE6tz2yucsHIDK3bo2qbIBqNyta5PdDECHpUOgzm2vfM7CAajcrWuTKhuAyt26NtnNAHRYOgTq3PbK5ywcgMrdujapsgGo3K1rk90MQIelQ6DOba98zsIBqNyta5MqG4DK3bo22c0AdFg6BOrc9srnLByAyt26NqmyAajcrWuT3QxAh6VDoM5tr3zOwgGo3K1rkyobgMrdujbZzQB0WDoE6tz2yucsHIDK3bo2qbIBqNyta5PdDECHpUOgzm2vfM7CAajcrWuTKhuAyt26NtnNAHRYOgTq3PbK5ywcgMrdujapsgGo3K1rk90MQIelQ6DOba98zsIBqNyta5MqG4DK3bo22c0AdFg6BOrc9srnLByAyt26NqmyAajcrWuT3QxAh6VDoM5tr3zOwgGo3K1rkyobgMrdujbZzQB0WDoE6tz2yucsHIDK3bo2qbIBqNyta5PdDECHpUOgzm2vfM7CAajcrWuTKhuAyt26NtnNAHRYOgTq3PbK5ywcgMrdujapsgGo3K1rk90MQIelQ6DOba98zsIBqNyta5MqG4DK3bo22c0AdFg6BOrc9srnLByAyt26NqmyAajcrWuT3QxAh6VDoM5tr3zOwgGo3K1rkyobgMrdujbZzQB0WDoE6tz2yucsHIDK3bo2qbIBqNyta5PdDECHpUOgzm2vfM7CAajcrWuTKhuAyt26NtnNAHRYOgTq3PbK5ywcgMrdujapsgGo3K1rk90MQIelQ6DOba98zsIBqNyta5MqG4DK3bo22c0AdFg6BOrc9srnLByAyt26NqmyAajcrWuT3QxAh6VDoM5tr3zOwgGo3K1rkyobgMrdujbZzQB0WDoE6tz2yucsHIDK3bo2qbIBqNyta5PdDECHpUOgzm2vfM7CAajcrWuTKhuAyt26NtnNAHRYOgTq3PbK5ywcgMrdujapsgGo3K1rk90MQIelQ6DOba98zsIBqNyta5MqG4DK3bo22c0AdFg6BOrc9srnLByAyt26NqmyAajcrWuT3QxAh6VDoM5tr3zOwgGo3K1rkyobgMrdujbZzQB0WDoE6tz2yucsHIDK3bo2qbIBqNyta5PdDECHpUOgzm2vfM7CAajcrWuTKhuAyt26NtnNAHRYOgTq3PbK5ywcgMrdujapsgGo3K1rk90MQIelQ6DOba98zsIBqNyta5MqG4DK3bo22c0AdFg6BOrc9srnLByAyt26NqmyAajcrWuT3QxAh6VDoM5tr3zOwgGo3K1rkyobgMrdujbZzQB0WDoE6tz2yucsHIDK3bo2qbIBqNyta5PdDECHpUOgzm2vfM7CAajcrWuTKhuAyt26NtnNAHRYOgTq3PbK5ywcgMrdujapsgGo3K1rk90MQIelQ6DOba98zsIBqNyta5MqG4DK3bo22c0AdFg6BOrc9srnLByAyt26NqmyAajcrWuT3QxAh6VDoM5tr3zOwgGo3K1rkyobgMrdujbZzQB0WDoE6tz2yucsHIDK3bo2qbIBqNyta5PdDECHpUOgzm2vfM7CAajcrWuTKhuAyt26NtnNAHRYOgTq3PbK5ywcgMrdujapsgGo3K1rk90MQIelQ6DOba98zsIBqNyta5MqG4DK3bo22c0AdFg6BOrc9srnLByAyt26NqmyAajcrWuT3QxAh6VDoM6tkioagMrdOqmyAajcPaMOCqAfehUAddoyjXnsArRnonUUC2WOGqAjXTLbtF6A+gKKD7EHugc3e6D7MYd1e27TQe+B6txWHdQe6MTlAWivG4BG4Qag+3JbBUAtUnEIgFZtAaBzuAHovtxWAVCLVBwCoFVbAOgcbgC6L7dVANQiFYcAaNUWADqHG4Duy20VALVIxSEAWrUFgM7hBqD7clsFQC1ScQiAVm0BoHO4Aei+3FYBUItUHAKgVVsA6BxuALovt1UA1CIVhwBo1RYAOocbgO7LbRUAtUjFIQBatQWAzuEGoPtyWwVALVJxCIBWbQGgc7gB6L7cVgFQi1QcAqBVWwDoHG4Aui+3VQDUIhWHAGjVFgA6hxuA7sttFQC1SMUhAFq1BYDO4Qag+3JbBUAtUnEIgFZtAaBzuAHovtxWAVCLVBwCoFVbAOgcbgC6L7dVANQiFYcAaNUWADqHG4Duy20VALVIxSEAWrUFgM7hBqD7clsFQC1ScQiAVm0BoHO4Aei+3FYBUItUHAKgVVsA6BxuALovt1UA1CIVhwBo1RYAOocbgO7LbRUAtUjFIQBatQWAzuEGoPtyWwVALVJxCIBWbQGgc7gB6L7cVgFQi1QcAqBVWwDoHG4Aui+3VQDUIhWHAGjVFgA6hxuA7sttFQC1SMUhAFq1BYDO4Qag+3JbBUAtUnEIgFZtAaBzuAHovtxWAVCLVBwCoFVbAOgcbgC6L7dVANQiFYcAaNUWADqHG4Duy20VALVIxSEAWrUFgM7hBqD7clsFQC1ScQiAVm0BoHO4Aei+3FYBUItUHAKgVVsA6BxuALovt1UA1CIVhwBo1RYAOocbgO7LbRUAtUjFIQBatQWAzuEGoPtyWwVALVJxCIBWbQGgc7gB6L7cVgFQi1QcAqBVWwDoHG4Aui+3VQDUIhWHAGjVFgA6hxuA7sttFQC1SMUhAFq1BYDO4Qag+3JbBUAtUnEIgFZtAaBzuAHovtxWAVCLVBwCoFVbAOgcbgC6L7dVANQiFYcAaNUWADqHG4Duy20VALVIxSEAWrUFgM7hBqD7clsFQC1ScQiAVm0BoHO4Aei+3FYBUItUHAKgVVsA6BxuALovt1UA1CIVhwBo1RYAOocbgO7LbRUAtUjFIQBatQWAzuEGoPtyWwVALVJxCIBWbQGgc7gB6L7cVgFQi1QcAqBVWwDoHG4Aui+3VQDUIhWHAGjVFgA6hxuA7sttFQC1SMUhAFq1BYDO4Qag+3JbBUAtUnEIgFZtAaBzuAHovtxWAVCLVBwCoFVbAOgcbgC6L7dVANQiFYcAaNUWADqHG4Duy20VALVIxSEAWrUFgM7hBqD7clsFQC1ScQiAVm0BoHO4Aei+3FYBUItUHAKgVVsA6BxuALovt1UA1CIVhwBo1RYAOocbgO7LbRUAtUjFIQBatQWAzuEGoPtyWwVALVJxCIBWbQGgc7gB6L7cVgFQi1QcAqBVWwDoHG4Aui+3VQDUIhWHAGjVFgA6hxuA7sttFQC1SMUhAFq1BYDO4Qag+3JbBUAtUnEIgFZtAaBzuAHovtxWAVCLVBwCoFVbAOgcbgC6L7dVANQiFYcAaNUWADqHG4Duy20VALVIxSEAWrUFgM7hBqD7clsFQC1ScQiAVm0BoHO4Aei+3FYBUItUHAKgVVsA6BxuALovt1UA1CIVhwBo1RYAOocbgO7LbRUAtUjFIQBatQWAzuEGoPtyWxUjQD//3ksnJ9/6iTn4xHEAaK8bgEbhBqD7clsVIUA/ezn/FfjST43BJw4EQHvdADQKNwDdl9uqCAH67slXfrL99PWTr/y6O/jEgQBorxuARuEGoPtyWxUfQD95Kd/3/OzlZ7/fHXziSAC01w1Ao3AD0H25rYoPoB+cfLX899vdwSeOBEB73QA0CjcA3ZfbqvgA+u7JH+f/flSCtDH4xJEAaK8bgEbhBqD7clsVHUA/f708dP/kpeok6G9V+rCryaOjrXbLNJrQBShd8iLPbTIASpfCy9hUjDYB0IMTAI1CADQCzQzQ7o1M3g/hR+wq8373/P1Fq486pnZpTVMVMFtwCG/PW89UecxexiH8bvCJQ61o3v1FA9DDzAagIbIBqLN5PVQAoDFkA9AQ2Qd+FX7ErjKvhwoANIZsABoiO8x9oN9u/dsYfOJIK5p3f9EA9DCzAWiI7AN/EmnErjKvhwoANIZsABoiOwRAP3/95Mt7ehZ+xK4yr4cKADSGbAAaIjsEQLef7u1tTCN2lXk9VACgMWQD0BDZQQC6/fR7KT+/1d3/XFPPo/p1A6AxZAPQENlhADo4+MTlVzTv/qIB6GFmA9AQ2QDU2bweKgDQGLIBaIhsAOpsXg8VAGgM2QA0RDYAdTavhwoANIZsABoiG4A6m9dDBQAaQzYADZENQJ3N66ECAI0hG4CGyAagzub1UAGAxpANQENkA1Bn83qoAEBjyAagIbIBqLN5PVQAoDFkA9AQ2QDU2bweKgDQGLIBaIhsAOpsXg8VAGgM2QA0RDYAdTavhwoANIZsABoiG4A6m9dDBQAaQzYADZENQJ3N66ECAI0hG4CGyAagzub1UAGAxpANQENkA1Bn83qoAEBjyAagIbIBqLN5PVQAoDFkA9AQ2QDU2bweKgDQGLIBaIhsAOpsXg8VAGgM2QA0RDYAdTavhwoANIZsABoie2EARcsUXYpBbExRCICuT3QpBrExRaEZATpV3WJWkh1ytV200qmKrE1rnarAbQKgq4p20kqnKrI2rXWqAOjqsiPbMtc6VZG1aa1TBUBXlx3ZlrnWqYqsTWudKgC6uuzItsy1TlVkbVrrVAHQ1WVHtmWudaoia9Nap2rNAEUIoZgFQBFCyFEAFCGEHAVAEULIUQAUIYQcBUARQshRABQhhBwFQBFCyFHhAPr59146OfnWT4Jk/+s/PDl5NlB2qk9e+sqvQ2VPVcA2Be5STG1a8cYUuE3BAPrZyyeZvvTTANk/zqNPnv1+gOxUn79+Es2WGbBNgbsUU5tWvDGFblMwgL578pWfbD8NsvIfnTz7R9ssO8gv3Hb7wUk0W2bANoXuUkxtWvHGFLpNoQD6yUv5hH/28v7/cqV/sv54m2cX/+5bn7wUz5YZrk2huxRTm1a8MQVvUyiAfnDy1fLfb+87+rOXyz+W7+4/e5v/yv2LaE6uhWtT4C5F1ab1bkzh2xQKoO+Wf7A+KnsfpoYQPX/35KvxXJ1YQJsCbZkxtWkBXVptmwIB9PPXy6ONgGsf4ogn+yX/yq+j2TIX0KYwXYqqTQvo0nrbtGKAfhDi73X+ixbLlrmENgXpUlxtWkCX1tum8AANdfXuoyB3XuRHOrFsmQtoU5guxdWm8F1acZvCAzTQ2n/00rMBLht+kF8xjGXLDN+mMF2KrE3Bu7TmNq0VoB8E+ZP5yUt5aixbZvA2helSbG0K3aVVt2mlV+F/HKblH5zsFOpga5rCtilQl6Jr0zo3pmW0Kdx9oN9u/btXff7uyZeDzPgiWj5FIdsUrEvRtWmdG9My2rTCJ5HyJ9+CHpvFcmwYtk2huxRPm1a9Ma30EP7z10++HOrx3Q/W3fIpCtim4F2Kp02r3phWCtDtp8FeIFO+uiZToAc3otkyA7YpfJciatOaN6a1AnT76ffSSf9WgFX/6CR0z+PZMsO1KXyXYmrTijem1QIUIYQiFwBFCCFHAVCEEHIUAEUIIUcBUIQQchQARQghRwFQhBByFABFCCFHAVCEEHIUAEUIIUcBUIQQchQARQghRwFQhBByFAA9eJ0mbV3Pv3fpwWyB9185TmOefk2d4FhlvcJXXnwwYaSHx/nc9P/osj3R43Ru7jzxI2+DoZkFQA9eXgF677fHfPefqoKObjlF1BqqcqSI5goffcc6UkvDAH18w8406XSOz15Rx4x/3ZBfAdCDl0+AjvvOulEKDaSNFdFa4YJ8SoCejqyJcDqlsz4Wh5YjALoSbe4k1qNQmUYRkPHzwovvp//13nO7/T/PaeMALX+++Zc3S4jrAOprl1AK0IfHHMTHIgC6Eu0HoCmDkmvVEilMdeDRAjQ79i7WWgXQdOpuj5olEu/3n/roFdqHAOhKtB+ApkfPl5tf6XZB9QCt1loF0PPE0w6hGKAPj5X77mhfAqArURugxaacf+9+eph7lO013k+PuZOrd8slHmWX0o92X+Y6a5zZ7Pt5tsPX3PIfPv2Nu+Zgg6mp+/rmrXTJi681q+z4R4vo7IHeHhxp9/XTRV4F0PO0gh+1xrtsL7wcvqj/qewcxoNqfaqJSxFsKfxRed9ClZgmcRY0DgHQlWgAoP/GaXWt5Y3WZevdtaBrjTEaCKh+fvTNZsh50rub2154MDUFzhfuFF8XuKuw1yqmp4hWkc1zoO/dHBipKvusOUAJ0PPjNj/Tb9+upnBgunYA/Z2bxbfzAYYB2in8XvXlLvdsztvMkEcB0JVoAKD5KctNSoMLydX3t5s3S3adFTtXH7/RgVMDRBff2W5TQLVOD571XnjvLDyYmgInyb9+I2kdeHeLaRQxUGStqw+27ZFaZWdnae/meVnZBUAfdvhZH8EPT9cOoCmZH2wf3SnG6wB0sPA0Ma0iH+3yLpNj+DgEQFeiIYDmm3i26V8uf5Bt6ukmXS581t6SSwSkyxcoSEdonh88S3out3QXHkxtfp2nFmlGMWURliJrXewcY7cqqb4uv8wBavCzsUM7VHgDoLfLBatD+qrAJkC7hZ9Vc3i6m8zaipYtALoSDQB0t+nuDt1bm3T32tNuT64iVvvCSy9AuwsPpqbUKH9QXjwv0oxidkUMFtlQ8xi7W8l5VW6xw5d90+RnPfhg4V0+138QegHaLfzM3N30c8kPzS8AuhINALS1xW9LlDSXbV85Pq0OWRu7So1hewHaXXgotTlWkZP/r1lM4wqYrchU773SOsbuVnLW2n3OAHr1RtK94l5jcLDwGqC7PUsLQI3Cz7PrSe+0QusotGwB0JVo8Cp8+XWTCPnJyJ2aROmCt7Oln/ecAzUWHkptHrcWP8nTzGLqA2ZbkVViTTijks5tRdlNrImxBi2A9hfeuApf/NwKULPw4oJU8fyBuQ5oyQKgK5FngPbv/GWXQ5rb/S/+7H1zYRtAq/3XM08ArS5r9e/L9gD04nF3F3QPAC1uiUrq+7cAaDQCoCvRRIAOnIEb2QPt3AeanxT0sAfaKaZ7wNxf5LYcuQXQsT3QSw/OundizQHQnsLfezV/CUv1FwSARiIAuhJNAejwGbiRc6DbNn6K3b+ec6ACgDbPgXaKMSjeX2SjhIFzoLuvi5LyK0vZpfbWeVwfAG1drB8sfHdXFOdA4xEAXYmmALS+ytzdkuv7KUvIdM56tp6Fv1ccoHYXtgG0zCrRU2GvU8yO4vYiy4EahOtWsrsKX9wrv7sPtHUQ37oKPxWgu0eYmlfhO4U3OtMhOlq+AOhKNAmg9bnM8/b+2Mh9oMUzNkfZ1ZBNfl7ves/CNoCWBOreB9oupr6d0lrktrhjvn2ne+99oNvd3a/lY0IteDXvA50E0B3Xz5PufaCtwne3MdV/CLgPNBYB0JVoEkDzR3Zeax1UFsq29c37rUd6Ohv6G80rJNdKU2thK0CTLzzYPnql/RK6bjHNIvqKbL8A9XZnpGbZ5z1PIu2u3Fc6b/BtEkCrB52yx/vLr3sLz3aTb6X/PrrT2EPmSaQ4BEBXomkArZ/Wbp+KO8++dbnx8/az8Jl+cZx0f2Y8Cz8I0GdKc7UjWqR3ijGK6BTZeiP9N3tHsj0Ln2O1MeDu5oDJAN1UT/a/WXw9VPjD3ZRVf414Fj4WAdCVaCJA093A7Kpw/YKgUvfSbf3yg231PqFr728NbX7wXAaE3ecRdRe2XkTK9j53r4RqvEOpVUyjiL4im5+J9L4xUrvsvrcxZQNcb43XeBtT73QNAHRbvJvp1u7rocI3b11Jv7ywK4u3MUUjAIqWoqWe+PP2PlCxeCV9NAKgaClaKkD3vz/IhyJFIwCKlqKlAnTvO4TsgMYjAIqWosUCdN97hOyAxiMAipai5QJ07HPh/YrPhY9IABQtRcsFaMq0/T0YtLnDAXw8AqAIIeQoAIoQQo4CoAgh5CgAihBCjgKgCCHkKACKEEKOAqAIIeQoAIoQQo4CoAgh5CgAihBCjgKgCCHkKACKEEKOAqAIIeQoAIoQQo4CoAgh5CgAihBCjgKgCPlV/THvxsd5niXXJ73efvPWtf4fnJafVb8de13+me0l1e6fHWIddnueDL1/2u5zVWuaHt/on3txGYOT3icAipBfeQToIIkA6GBeNf3OszJcfY8AKEJ+NfKhmgcBULuCAnQS/0ZHGxMARcivAGhQgKpXCoAiFFBdgN5/Lj2gvPBi9kmbu0P4cis/y7bV8+T6vePk4ne220evpIefT9+tjJs72aFotjU/eiX9j6t36zFTgN5/KjnKBi0B2lrk/s008dZ2WxHr4fHRdyrro6ycK7eKUa4/TJe8WnwI6G6ExzfypfPi8s8ITdPuPdUqIB/W/PZ2m67J0a0SQY0Vr9bRLGfzRpJcvJt/v28izpPiI0rPDKrtRq+nqZi0eux6stLVPPrmdvuL4+TCa33l1/1oTnrdjcaktQVAEfKrDkDfKM+HZtvkAECfSbfUlBPl2bujaudyty2fd36QbfAvVIMWAG0tclYkXi8TM2DtrNUpwsv5KM/kX+WMaoxQ7N+e5t/PSjxNrnROK5YE6n47/VamF/LRmyteraNRTrGOR88XADUnovzzkC5Wr3xnWtsAfXzj0l/dbP4Zyor6Rj7g9by6jK9G+T0AbXajOWltAVCE/KoN0PPkKNvnubfbcHsAWhDs8Y3kWro/9WYDSOflTmBy9f1sV63et0pRcOlutrt3uxivtUj6xRcepInZOEVi0gRW+rPt/XwfsDFKa4ScL6mrQMvtbLnLD8rlCpUE6n47Xdlb281pQf3milfr2C0n/Ua61KObSQHQnokoYG6cp+iMXpNtdwmv+dcmHS8dLivu0Z1sWaP8dj8ud4poTVpbABQhv6qvwhd7efmmWexCDQC02C6ro9TGecLzcr+1+MFpzYnTgrJnKXHy8VqLlF+clon2SKTJAAAE/ElEQVRpdH1WsDw8r0bJgZUX0xzh4XG+B3YlXbSstl5uW9dofrv4z00OqdaK1+vYLqfcs0yn7PrARJyXe7Cdnb/O6PVPzzNabj9+pfXXJquzyCjOCRjlmwBtFtGatLYAKEJ+1QZopo9/+ac3EwtA8w15d/IuO+tYGvNteXf4Wp0P3O42+Gys7P9bi7QOd8+yA9fGVZX0hxff2e5GuVxV0RkhZ/I3jm8XxTSWq4fdmt+uQLP7zm7F61OZ7XKqdT0tWNkzEcW+52n3CL4zeg3Q+g9BB+tlcU2A1uUbAG0V0Zq0tgAoQn7VOQeaHZ9Wx5R2gJq3j54XJznLbblxHFsCJdvMC4g2FmntL51VJ0Nb37j4ZzW2iiraIafpF6dP/K83rleoqautRun7dlVgAbTmitcAbZdz3t7Z7JuI0+o0RVvt0c2Tk8Zfmx6A1uX3ALRZRHPS2gKgCPlVG6DZ/ujR0994x3YIn2/+1RM0PQAtv7YBtLFIizbppv9M+9RdduU5yY9z2wBthpwntx/fuPR/37lc7IhJAVqt+Xl1EWa34jUo2+V0ANo3Edl3TUL2jj7Qheo06hSAdopoTFpbABQhv2oBNDsfWO5WjQLUPNHmYw/0snEL0OYHzxX7gYN7oA+Pr6f/tz299K9utK9R18OO7YG2V7wGZbucxqH90ESkJH9gHMH3j95U40SIG0A73dhNWlsAFCG/agG02hCLS882gJp36oyfA82iRs+Bti97F9q8meTnNndVtEM2dy7dS78+f+J/aV4IEwC0PgfaXvHmnmaznM450N6JOD36izvdI/j+0betyWpccZsO0L5uFJPWFgBFyK96AXqeFJe6S4AW0CkuV9dwKTbPBictV+HLu4Iubwevwp/tbthpjFgVV14cut5YsBly+sQ/Set++O88X94RKgNo8yp8e8X7znVutztOpRy8PjgR2e2h3T3M/tEbk9QEoACgZj+aRbQmrS0AipBf9RzCZzchtgCabvj5PYlNgKYUuXR327mvsrwB5+r73Ttz8nsYk/Z9oOUixX2g94+rfd5t486h9D+zkEd3uvtd7ZDilsy0ovwrMUDzW4WyZ4sud1a8BdDmjUxnWcLuPtDeichOR3YPnbuj12BLS7j2IBux/pYAoO1+XHrQLqI1aW0BUIT8qnsjfa5Lb1b7gznwiou8xRHybv+pfBYoudYYKgfZ8JNI12xPIhU7UtfbJVU3WV3snttsjZAuVRzHNu/0HAdomfxCyaR6xVsAbZbTfhKpdyLSRYq/HM0nQFujl9PULCF5on4USQDQZj/ak54X0Zy0tgAoQn7VuY2pfC59t+EWwNu8kj0Cft4CaPn09WsN8y+OczL0PQufP3W+lT0L3zz6z0OKJ9TbBGyOUO4knlZuKUCzy9XVs/DNFW8DtFlO8Sz8aQugnYmojqabAG2NXk1TaX4uJXLzerkAoM1+1JNeF9GYtLYAKEIotHpvlK+0ezTr8deM+xRCC4AihEKp3Fu3PCvZfL/SmfxFgPsSAEUIhdLjG/nFoFPz/qBa96oj+82fLG4HFIAihMLpXnF1ZngH9NT2w/ACoAihcMrei3zUeidzW28Wl8qWKgCKEEKOAqAIIeQoAIoQQo4CoAgh5CgAihBCjgKgCCHkKACKEEKOAqAIIeQoAIoQQo4CoAgh5CgAihBCjgKgCCHkKACKEEKOAqAIIeSo/x/OFd45TuPYdAAAAABJRU5ErkJggg==" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot1d.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
</section>
</section>
<section id="tcb-stratified-by-of-instantaneous-bt-groups" class="level1" data-number="4">
<h1 data-number="4"><span class="header-section-number">4</span> TCB stratified by % of instantaneous BT groups</h1>
<section id="plot-2-time-to-block-vs.-instantaneous-bt-by-bt" class="level2" data-number="4.1">
<h2 data-number="4.1" class="anchored" data-anchor-id="plot-2-time-to-block-vs.-instantaneous-bt-by-bt"><span class="header-section-number">4.1</span> Plot 2 (time to block vs. instantaneous BT, by % BT)</h2>
<p>This plot gets at whether both instantaneous BT (<code>inst_bt</code>) and the percentage of instantaneous BT applied (<code>pct_ib</code>) are important parts of the model to predict <code>time_block</code>.</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcb_b, <span class="fu">aes</span>(<span class="at">x =</span> inst_bt, <span class="at">y =</span> time_block, <span class="at">col =</span> pct_ib)) <span class="sc">+</span></span>
<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">formula =</span> y <span class="sc">~</span> x, <span class="at">se =</span> <span class="cn">FALSE</span>) <span class="sc">+</span></span>
<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span> pct_ib, <span class="at">labeller =</span> <span class="st">&quot;label_both&quot;</span>) <span class="sc">+</span></span>
<span id="cb22-5"><a href="#cb22-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb22-6"><a href="#cb22-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Block, by Instantaneous BT (mA)&quot;</span>,</span>
<span id="cb22-7"><a href="#cb22-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="st">&quot;Stratified by % of Instantaneous BT&quot;</span>,</span>
<span id="cb22-8"><a href="#cb22-8" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption =</span> <span class="st">&quot;Experiments where Complete Block was not achieved are excluded&quot;</span>,</span>
<span id="cb22-9"><a href="#cb22-9" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Instantaneous BT (mA)&quot;</span>,</span>
<span id="cb22-10"><a href="#cb22-10" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Block (seconds)&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot2.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
</section>
<section id="model-for-plot-2" class="level2" data-number="4.2">
<h2 data-number="4.2" class="anchored" data-anchor-id="model-for-plot-2"><span class="header-section-number">4.2</span> Model for Plot 2</h2>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>m2 <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_block <span class="sc">~</span> inst_bt <span class="sc">+</span> pct_ib, <span class="at">data =</span> tcb_b)</span>
<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>m2t <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m2, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-5"><a href="#cb25-5" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m2t, <span class="at">digits =</span> <span class="dv">3</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: left;">term</th>
<th style="text-align: right;">estimate</th>
<th style="text-align: right;">std.error</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">conf.low</th>
<th style="text-align: right;">conf.high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">(Intercept)</td>
<td style="text-align: right;">211.666</td>
<td style="text-align: right;">27.607</td>
<td style="text-align: right;">0.000</td>
<td style="text-align: right;">155.561</td>
<td style="text-align: right;">267.771</td>
</tr>
<tr class="even">
<td style="text-align: left;">inst_bt</td>
<td style="text-align: right;">-37.693</td>
<td style="text-align: right;">9.797</td>
<td style="text-align: right;">0.001</td>
<td style="text-align: right;">-57.602</td>
<td style="text-align: right;">-17.784</td>
</tr>
<tr class="odd">
<td style="text-align: left;">pct_ib60</td>
<td style="text-align: right;">-46.434</td>
<td style="text-align: right;">25.094</td>
<td style="text-align: right;">0.073</td>
<td style="text-align: right;">-97.432</td>
<td style="text-align: right;">4.563</td>
</tr>
<tr class="even">
<td style="text-align: left;">pct_ib80</td>
<td style="text-align: right;">-95.509</td>
<td style="text-align: right;">24.559</td>
<td style="text-align: right;">0.000</td>
<td style="text-align: right;">-145.419</td>
<td style="text-align: right;">-45.598</td>
</tr>
</tbody>
</table>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m2) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: right;">r.squared</th>
<th style="text-align: right;">adj.r.squared</th>
<th style="text-align: right;">sigma</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">df</th>
<th style="text-align: right;">df.residual</th>
<th style="text-align: right;">nobs</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: right;">0.4588281</td>
<td style="text-align: right;">0.4110777</td>
<td style="text-align: right;">56.60737</td>
<td style="text-align: right;">9.73e-05</td>
<td style="text-align: right;">3</td>
<td style="text-align: right;">34</td>
<td style="text-align: right;">38</td>
</tr>
</tbody>
</table>
</div>
</div>
</section>
<section id="what-is-the-effect-of-adding-pct_ib-to-a-model-with-just-inst_bt" class="level2" data-number="4.3">
<h2 data-number="4.3" class="anchored" data-anchor-id="what-is-the-effect-of-adding-pct_ib-to-a-model-with-just-inst_bt"><span class="header-section-number">4.3</span> What is the effect of adding <code>pct_ib</code> to a model with just <code>inst_bt</code>?</h2>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>m2a <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_block <span class="sc">~</span> inst_bt, <span class="at">data =</span> tcb_b)</span>
<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>m2at <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m2a, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb27-4"><a href="#cb27-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb27-5"><a href="#cb27-5" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m2at, <span class="at">digits =</span> <span class="dv">3</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: left;">term</th>
<th style="text-align: right;">estimate</th>
<th style="text-align: right;">std.error</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">conf.low</th>
<th style="text-align: right;">conf.high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">(Intercept)</td>
<td style="text-align: right;">149.534</td>
<td style="text-align: right;">23.937</td>
<td style="text-align: right;">0.000</td>
<td style="text-align: right;">100.987</td>
<td style="text-align: right;">198.081</td>
</tr>
<tr class="even">
<td style="text-align: left;">inst_bt</td>
<td style="text-align: right;">-35.100</td>
<td style="text-align: right;">11.512</td>
<td style="text-align: right;">0.004</td>
<td style="text-align: right;">-58.447</td>
<td style="text-align: right;">-11.753</td>
</tr>
</tbody>
</table>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m2a) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: right;">r.squared</th>
<th style="text-align: right;">adj.r.squared</th>
<th style="text-align: right;">sigma</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">df</th>
<th style="text-align: right;">df.residual</th>
<th style="text-align: right;">nobs</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: right;">0.2052426</td>
<td style="text-align: right;">0.183166</td>
<td style="text-align: right;">66.66699</td>
<td style="text-align: right;">0.0042871</td>
<td style="text-align: right;">1</td>
<td style="text-align: right;">36</td>
<td style="text-align: right;">38</td>
</tr>
</tbody>
</table>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb29"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="fu">anova</span>(m2a, m2)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>Analysis of Variance Table

Model 1: time_block ~ inst_bt
Model 2: time_block ~ inst_bt + pct_ib
  Res.Df    RSS Df Sum of Sq     F   Pr(&gt;F)   
1     36 160002                               
2     34 108949  2     51052 7.966 0.001454 **
---
Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
</div>
</div>
<p>Model 2a tells us that <code>inst_bt</code>, by itself, has a meaningful effect (p = 0.004) on time to complete block across these 38 experiments. The equation is</p>
<ul>
<li><strong>time to block = 149.5 - 35.1 (<code>inst_bt</code>)</strong>,</li>
</ul>
<p>with a 95% CI for the slope of (-58.5, -11.8).</p>
<p>The ANOVA table tells us that adding the <code>pct_ib</code> category (40, 60 or 80) adds meaningful predictive value to Model 2a, with an associated <em>p</em> value of 0.001.</p>
<p>Specifically this yields model 2, which is</p>
<ul>
<li><strong>time to block = 211.7 - 37.7 (<code>inst_bt</code>) - 46.4 (<code>pct_ib</code> = 60) - 95.5 (<code>pct_ib</code> = 80)</strong>,</li>
</ul>
</section>
<section id="new-r-square-values-for-predicting-tcb" class="level2" data-number="4.4">
<h2 data-number="4.4" class="anchored" data-anchor-id="new-r-square-values-for-predicting-tcb"><span class="header-section-number">4.4</span> NEW! R-square values for predicting TCB</h2>
<p>Using the categorical version of <code>pct_ib</code>…</p>
<table class="table">
<colgroup>
<col style="width: 37%">
<col style="width: 20%">
<col style="width: 13%">
<col style="width: 13%">
<col style="width: 13%">
</colgroup>
<thead>
<tr class="header">
<th style="text-align: right;">Model</th>
<th style="text-align: right;">R-square</th>
<th style="text-align: right;">p for <code>inst_bt</code></th>
<th style="text-align: right;">p for <code>pct_ib</code></th>
<th style="text-align: right;">p for model</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: right;">Model for TCB using <code>inst_bt</code> and <code>pct_ib</code></td>
<td style="text-align: right;">0.459</td>
<td style="text-align: right;">0.005</td>
<td style="text-align: right;">0.001</td>
<td style="text-align: right;">&lt; 0.001</td>
</tr>
<tr class="even">
<td style="text-align: right;">Model including only <code>inst_bt</code></td>
<td style="text-align: right;">0.205</td>
<td style="text-align: right;">0.004</td>
<td style="text-align: right;">–</td>
<td style="text-align: right;">0.004</td>
</tr>
<tr class="odd">
<td style="text-align: right;">Model including only <code>pct_ib</code></td>
<td style="text-align: right;">0.223</td>
<td style="text-align: right;">–</td>
<td style="text-align: right;">0.012</td>
<td style="text-align: right;">0.012</td>
</tr>
</tbody>
</table>
</section>
<section id="model-treating-both-inst_bt-and-pct_ib-as-numeric" class="level2" data-number="4.5">
<h2 data-number="4.5" class="anchored" data-anchor-id="model-treating-both-inst_bt-and-pct_ib-as-numeric"><span class="header-section-number">4.5</span> Model treating both <code>inst_bt</code> and <code>pct_ib</code> as numeric</h2>
<p>If instead, we wanted to treat both <code>inst_bt</code> and <code>pct_ib</code> as numeric rather than as categories, we’d have model 2B, as shown below.</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb31"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a>m2B <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_block <span class="sc">~</span> inst_bt <span class="sc">+</span> <span class="fu">as.numeric</span>(pct_ib), <span class="at">data =</span> tcb_b)</span>
<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a>m2Bt <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m2B, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-5"><a href="#cb31-5" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m2Bt, <span class="at">digits =</span> <span class="dv">3</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: left;">term</th>
<th style="text-align: right;">estimate</th>
<th style="text-align: right;">std.error</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">conf.low</th>
<th style="text-align: right;">conf.high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">(Intercept)</td>
<td style="text-align: right;">260.276</td>
<td style="text-align: right;">33.904</td>
<td style="text-align: right;">0</td>
<td style="text-align: right;">191.448</td>
<td style="text-align: right;">329.105</td>
</tr>
<tr class="even">
<td style="text-align: left;">inst_bt</td>
<td style="text-align: right;">-37.685</td>
<td style="text-align: right;">9.656</td>
<td style="text-align: right;">0</td>
<td style="text-align: right;">-57.288</td>
<td style="text-align: right;">-18.083</td>
</tr>
<tr class="odd">
<td style="text-align: left;">as.numeric(pct_ib)</td>
<td style="text-align: right;">-47.928</td>
<td style="text-align: right;">11.837</td>
<td style="text-align: right;">0</td>
<td style="text-align: right;">-71.959</td>
<td style="text-align: right;">-23.897</td>
</tr>
</tbody>
</table>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb32"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m2B) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: right;">r.squared</th>
<th style="text-align: right;">adj.r.squared</th>
<th style="text-align: right;">sigma</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">df</th>
<th style="text-align: right;">df.residual</th>
<th style="text-align: right;">nobs</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: right;">0.458755</td>
<td style="text-align: right;">0.4278267</td>
<td style="text-align: right;">55.7966</td>
<td style="text-align: right;">2.16e-05</td>
<td style="text-align: right;">2</td>
<td style="text-align: right;">35</td>
<td style="text-align: right;">38</td>
</tr>
</tbody>
</table>
</div>
</div>
<p>This yields a p value &lt; 0.001 for adding <code>inst_bt</code> to a model with <code>pct_ib</code> alone, and the same p value (&lt; 0.001) for adding <code>pct_ib</code> to a model with <code>inst_bt</code> alone. In other words, we need both elements of the product.</p>
</section>
<section id="plot-3-including-the-cases-where-complete-block-was-not-achieved" class="level2" data-number="4.6">
<h2 data-number="4.6" class="anchored" data-anchor-id="plot-3-including-the-cases-where-complete-block-was-not-achieved"><span class="header-section-number">4.6</span> Plot 3 (<em>including</em> the cases where complete block was not achieved)</h2>
<p>I am retaining this plot in case you want to be able to show the values of <code>pct_ib</code> and <code>abs_block</code> for the experiments which failed to achieve a complete block.</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb33"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcb, <span class="fu">aes</span>(<span class="at">x =</span> abs_block, <span class="at">y =</span> time_block, <span class="at">col =</span> pct_ib)) <span class="sc">+</span></span>
<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">data =</span> tcb <span class="sc">|&gt;</span> <span class="fu">filter</span>(time_block <span class="sc">&lt;</span> <span class="dv">300</span>), <span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">data =</span> tcb <span class="sc">|&gt;</span> <span class="fu">filter</span>(time_block <span class="sc">==</span> <span class="dv">300</span>), </span>
<span id="cb33-4"><a href="#cb33-4" aria-hidden="true" tabindex="-1"></a>             <span class="fu">aes</span>(<span class="at">fill =</span> pct_ib), <span class="at">shape =</span> <span class="st">&quot;X&quot;</span>, <span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb33-5"><a href="#cb33-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span> pct_ib, <span class="at">labeller =</span> <span class="st">&quot;label_both&quot;</span>) <span class="sc">+</span></span>
<span id="cb33-6"><a href="#cb33-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color =</span> <span class="st">&quot;none&quot;</span>, <span class="at">fill =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb33-7"><a href="#cb33-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Block, by Absolute Block Value&quot;</span>,</span>
<span id="cb33-8"><a href="#cb33-8" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="st">&quot;Stratified by % of Instantaneous BT&quot;</span>,</span>
<span id="cb33-9"><a href="#cb33-9" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption =</span> <span class="st">&quot;Experiments where Complete Block was not achieved indicated by X at Time = 300 seconds&quot;</span>,</span>
<span id="cb33-10"><a href="#cb33-10" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Absolute Block Value (mA)&quot;</span>,</span>
<span id="cb33-11"><a href="#cb33-11" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Block (seconds)&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img 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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb34"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot3.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
</section>
<section id="plot-3a-excluding-the-cases-where-complete-block-was-not-achieved" class="level2" data-number="4.7">
<h2 data-number="4.7" class="anchored" data-anchor-id="plot-3a-excluding-the-cases-where-complete-block-was-not-achieved"><span class="header-section-number">4.7</span> Plot 3A (<em>excluding</em> the cases where complete block was not achieved)</h2>
<p>Plot 3A is the one we’ve seen before, which looks at separate lines predicting time to complete block, by absolute block value.</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb36"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcb_b, <span class="fu">aes</span>(<span class="at">x =</span> abs_block, <span class="at">y =</span> time_block, <span class="at">col =</span> pct_ib)) <span class="sc">+</span></span>
<span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">formula =</span> y <span class="sc">~</span> x, <span class="at">se =</span> <span class="cn">FALSE</span>) <span class="sc">+</span></span>
<span id="cb36-4"><a href="#cb36-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span> pct_ib, <span class="at">labeller =</span> <span class="st">&quot;label_both&quot;</span>) <span class="sc">+</span></span>
<span id="cb36-5"><a href="#cb36-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb36-6"><a href="#cb36-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Block, by Absolute Block Value&quot;</span>,</span>
<span id="cb36-7"><a href="#cb36-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="st">&quot;Stratified by % of Instantaneous BT&quot;</span>,</span>
<span id="cb36-8"><a href="#cb36-8" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption =</span> <span class="st">&quot;Experiments where Complete Block was not achieved are excluded&quot;</span>,</span>
<span id="cb36-9"><a href="#cb36-9" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Absolute Block Value (mA)&quot;</span>,</span>
<span id="cb36-10"><a href="#cb36-10" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Block (seconds)&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb37"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot3a.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
</section>
<section id="model-for-plot-3a" class="level2" data-number="4.8">
<h2 data-number="4.8" class="anchored" data-anchor-id="model-for-plot-3a"><span class="header-section-number">4.8</span> Model for Plot 3A</h2>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb39"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb39-1"><a href="#cb39-1" aria-hidden="true" tabindex="-1"></a>m3a <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_block <span class="sc">~</span> abs_block <span class="sc">+</span> pct_ib, <span class="at">data =</span> tcb_b)</span>
<span id="cb39-2"><a href="#cb39-2" aria-hidden="true" tabindex="-1"></a>m3at <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m3a, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb39-3"><a href="#cb39-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb39-4"><a href="#cb39-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb39-5"><a href="#cb39-5" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m3at, <span class="at">digits =</span> <span class="dv">3</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: left;">term</th>
<th style="text-align: right;">estimate</th>
<th style="text-align: right;">std.error</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">conf.low</th>
<th style="text-align: right;">conf.high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">(Intercept)</td>
<td style="text-align: right;">179.758</td>
<td style="text-align: right;">23.551</td>
<td style="text-align: right;">0.000</td>
<td style="text-align: right;">131.897</td>
<td style="text-align: right;">227.620</td>
</tr>
<tr class="even">
<td style="text-align: left;">abs_block</td>
<td style="text-align: right;">-53.138</td>
<td style="text-align: right;">14.972</td>
<td style="text-align: right;">0.001</td>
<td style="text-align: right;">-83.564</td>
<td style="text-align: right;">-22.712</td>
</tr>
<tr class="odd">
<td style="text-align: left;">pct_ib60</td>
<td style="text-align: right;">-25.485</td>
<td style="text-align: right;">26.220</td>
<td style="text-align: right;">0.338</td>
<td style="text-align: right;">-78.771</td>
<td style="text-align: right;">27.801</td>
</tr>
<tr class="even">
<td style="text-align: left;">pct_ib80</td>
<td style="text-align: right;">-55.002</td>
<td style="text-align: right;">26.915</td>
<td style="text-align: right;">0.049</td>
<td style="text-align: right;">-109.701</td>
<td style="text-align: right;">-0.304</td>
</tr>
</tbody>
</table>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb40"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m3a) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb40-3"><a href="#cb40-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: right;">r.squared</th>
<th style="text-align: right;">adj.r.squared</th>
<th style="text-align: right;">sigma</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">df</th>
<th style="text-align: right;">df.residual</th>
<th style="text-align: right;">nobs</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: right;">0.433206</td>
<td style="text-align: right;">0.3831948</td>
<td style="text-align: right;">57.93193</td>
<td style="text-align: right;">0.0002083</td>
<td style="text-align: right;">3</td>
<td style="text-align: right;">34</td>
<td style="text-align: right;">38</td>
</tr>
</tbody>
</table>
</div>
</div>
<p>I’m not sure whether this model is still relevant. I think we now prefer the model for Plot 2.</p>
</section>
</section>
<section id="time-to-complete-recovery-data" class="level1" data-number="5">
<h1 data-number="5"><span class="header-section-number">5</span> Time to Complete Recovery Data</h1>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb41"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a>tcr <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(<span class="st">&quot;EMGReflex_Induction_Recovery_TCR-2024-05-27_revised.csv&quot;</span>, </span>
<span id="cb41-2"><a href="#cb41-2" aria-hidden="true" tabindex="-1"></a>                <span class="at">show_col_types =</span> F) <span class="sc">|&gt;</span> </span>
<span id="cb41-3"><a href="#cb41-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">clean_names</span>() <span class="sc">|&gt;</span></span>
<span id="cb41-4"><a href="#cb41-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">date =</span> <span class="fu">mdy</span>(date)) <span class="sc">|&gt;</span></span>
<span id="cb41-5"><a href="#cb41-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">set =</span> <span class="fu">factor</span>(set),</span>
<span id="cb41-6"><a href="#cb41-6" aria-hidden="true" tabindex="-1"></a>         <span class="at">charge =</span> <span class="fu">as.numeric</span>(block_period <span class="sc">*</span> bt),</span>
<span id="cb41-7"><a href="#cb41-7" aria-hidden="true" tabindex="-1"></a>         <span class="at">block_period =</span> <span class="fu">factor</span>(block_period),</span>
<span id="cb41-8"><a href="#cb41-8" aria-hidden="true" tabindex="-1"></a>         <span class="at">animal =</span> <span class="fu">factor</span>(animal))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
<section id="codebook-for-tcr-data" class="level2" data-number="5.1">
<h2 data-number="5.1" class="anchored" data-anchor-id="codebook-for-tcr-data"><span class="header-section-number">5.1</span> Codebook for TCR data</h2>
<p>We have 45 rows and 7 variables, with 0 missing observations.</p>
<table class="table">
<colgroup>
<col style="width: 17%">
<col style="width: 82%">
</colgroup>
<thead>
<tr class="header">
<th style="text-align: center;">Variable</th>
<th style="text-align: left;">Description</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: center;"><code>animal</code></td>
<td style="text-align: left;">EMG_Reflex 086, 092, 094, 095, 106 and 109</td>
</tr>
<tr class="even">
<td style="text-align: center;"><code>date</code></td>
<td style="text-align: left;">between 2022-11-22 and 2023-06-06</td>
</tr>
<tr class="odd">
<td style="text-align: center;"><code>set</code></td>
<td style="text-align: left;">1, 2 or 3 (consistent in <code>date</code>)</td>
</tr>
<tr class="even">
<td style="text-align: center;"><code>bt</code></td>
<td style="text-align: left;">BT (mA): consistent within <code>set</code> for each <code>animal</code></td>
</tr>
<tr class="odd">
<td style="text-align: center;"><code>block_period</code></td>
<td style="text-align: left;">Block period (in minutes): 1, 2 or 3</td>
</tr>
<tr class="even">
<td style="text-align: center;"><code>time_recover</code></td>
<td style="text-align: left;">Time to Complete Recovery (seconds): quantitative between 2 and 592 with all quantities ending in 2.</td>
</tr>
<tr class="odd">
<td style="text-align: center;"><code>charge</code></td>
<td style="text-align: left;"><code>block_period</code> times <code>bt</code></td>
</tr>
</tbody>
</table>
<p>Each “run” is identified by an <code>animal</code>, a <code>set</code> (1, 2, or 3) and <code>date</code>. Each <code>animal</code>-<code>set</code> combination yields three runs, all done on the same <code>date</code>, with the same <code>bt</code>, at the three levels of the experimental setting <code>block_period</code>.</p>
<p>The outcome of interest is time to complete recovery, measured in seconds.</p>
</section>
<section id="current-analytic-goals-for-tcr" class="level2" data-number="5.2">
<h2 data-number="5.2" class="anchored" data-anchor-id="current-analytic-goals-for-tcr"><span class="header-section-number">5.2</span> Current analytic goals for TCR</h2>
<p>Using all of the TCR data,</p>
<ol type="1">
<li>Is time to recovery (<code>time_recover</code>) reasonably well-described using a linear function of <code>charge</code>?</li>
<li>If that’s true, then which of the two elements: <code>block_period</code> or <code>bt</code> (whose product is equal to <code>charge</code>) is the driver of that effect? Or is it truly the combination?</li>
</ol>
</section>
</section>
<section id="tcr-vs.-charge-overall" class="level1" data-number="6">
<h1 data-number="6"><span class="header-section-number">6</span> TCR vs. <code>charge</code>, overall</h1>
<p>Is time to recovery (<code>time_recover</code>) reasonably well-described using a linear function of <code>charge</code>?</p>
<section id="plot-4-linear-fit" class="level2" data-number="6.1">
<h2 data-number="6.1" class="anchored" data-anchor-id="plot-4-linear-fit"><span class="header-section-number">6.1</span> Plot 4 (Linear Fit)</h2>
<p>This plot includes all 45 experiments included in the TCR data.</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb42"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcr, <span class="fu">aes</span>(<span class="at">x =</span> charge, <span class="at">y =</span> time_recover)) <span class="sc">+</span></span>
<span id="cb42-2"><a href="#cb42-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb42-3"><a href="#cb42-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, <span class="at">formula =</span> y <span class="sc">~</span> x) <span class="sc">+</span></span>
<span id="cb42-4"><a href="#cb42-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Recovery, by Charge&quot;</span>,</span>
<span id="cb42-5"><a href="#cb42-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">glue</span>(<span class="fu">nrow</span>(tcr), <span class="st">&quot; experiments on time to recovery&quot;</span>),</span>
<span id="cb42-6"><a href="#cb42-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Charge&quot;</span>,</span>
<span id="cb42-7"><a href="#cb42-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Recovery (seconds)&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img 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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb43"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot4.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
<p>The linear model shown in plot 4 is summarized below:</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb45"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb45-1"><a href="#cb45-1" aria-hidden="true" tabindex="-1"></a>m4 <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_recover <span class="sc">~</span> charge, <span class="at">data =</span> tcr)</span>
<span id="cb45-2"><a href="#cb45-2" aria-hidden="true" tabindex="-1"></a>m4t <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m4, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb45-3"><a href="#cb45-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb45-4"><a href="#cb45-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb45-5"><a href="#cb45-5" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m4t, <span class="at">digits =</span> <span class="dv">3</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: left;">term</th>
<th style="text-align: right;">estimate</th>
<th style="text-align: right;">std.error</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">conf.low</th>
<th style="text-align: right;">conf.high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">(Intercept)</td>
<td style="text-align: right;">-6.368</td>
<td style="text-align: right;">25.376</td>
<td style="text-align: right;">0.803</td>
<td style="text-align: right;">-57.544</td>
<td style="text-align: right;">44.809</td>
</tr>
<tr class="even">
<td style="text-align: left;">charge</td>
<td style="text-align: right;">41.767</td>
<td style="text-align: right;">7.430</td>
<td style="text-align: right;">0.000</td>
<td style="text-align: right;">26.783</td>
<td style="text-align: right;">56.751</td>
</tr>
</tbody>
</table>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb46"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb46-1"><a href="#cb46-1" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m4) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb46-2"><a href="#cb46-2" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb46-3"><a href="#cb46-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: right;">r.squared</th>
<th style="text-align: right;">adj.r.squared</th>
<th style="text-align: right;">sigma</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">df</th>
<th style="text-align: right;">df.residual</th>
<th style="text-align: right;">nobs</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: right;">0.4235883</td>
<td style="text-align: right;">0.4101833</td>
<td style="text-align: right;">93.80866</td>
<td style="text-align: right;">1.3e-06</td>
<td style="text-align: right;">1</td>
<td style="text-align: right;">43</td>
<td style="text-align: right;">45</td>
</tr>
</tbody>
</table>
</div>
</div>
</section>
<section id="new-plot-4a.-grouped" class="level2" data-number="6.2">
<h2 data-number="6.2" class="anchored" data-anchor-id="new-plot-4a.-grouped"><span class="header-section-number">6.2</span> NEW! Plot 4A. (Grouped)</h2>
<p>Again, this plot includes all 45 experiments included in the TCR data.</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb47"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb47-1"><a href="#cb47-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcr, <span class="fu">aes</span>(<span class="at">x =</span> charge, <span class="at">y =</span> time_recover, <span class="at">col =</span> block_period, <span class="at">group =</span> block_period)) <span class="sc">+</span></span>
<span id="cb47-2"><a href="#cb47-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb47-3"><a href="#cb47-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, <span class="at">formula =</span> y <span class="sc">~</span> x) <span class="sc">+</span></span>
<span id="cb47-4"><a href="#cb47-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Recovery, by Charge&quot;</span>,</span>
<span id="cb47-5"><a href="#cb47-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">glue</span>(<span class="fu">nrow</span>(tcr), <span class="st">&quot; experiments on time to recovery&quot;</span>),</span>
<span id="cb47-6"><a href="#cb47-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Charge&quot;</span>,</span>
<span id="cb47-7"><a href="#cb47-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Recovery (seconds)&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAMAAADDuCPrAAABelBMVEUAAAAAADoAAGYAOjoAOmYAOpAAZpAAZrYAujgzMzM6AAA6OgA6Ojo6OmY6OpA6ZmY6ZpA6ZrY6kJA6kLY6kNtNTU1NTW5NTY5Nbm5Nbo5NbqtNjshhnP9mAABmADpmOgBmOjpmOmZmZgBmZjpmZmZmZpBmkGZmkJBmkLZmkNtmtrZmtttmtv9uTU1ubk1ubo5ujqtujshuq+SOTU2Obk2Obm6Oq6uOyOSOyP+QOgCQOjqQZgCQZjqQZmaQZpCQkDqQkGaQkLaQtmaQtpCQtraQttuQ27aQ29uQ2/+rbk2rjm6ryOSr5P+2ZgC2Zjq2Zma2kDq2kGa2kJC2tpC2tra2ttu229u22/+2/7a2///Ijk3Ijm7Iq27I5P/I///bkDrbkGbbtmbbtpDbtrbbttvb27bb29vb2//b/7bb/9vb///kq27kyI7kyKvk5Mjk///r6+v4dm3/tmb/yI7/25D/27b/29v/5Kv/5Mj//7b//8j//9v//+T////SigcxAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nO29/YMU17nfWQPDIjejC1wzciyD0Ub4Wg6Q+FqbXONIib172VjOHSLlYmnBkcfgxSQoWYthQEJN/e/b9db91Muprpdz+qnu5/P5AaZ7qupbT8/UZ07VOXUqigEAYBCR9g4AAGwrCBQAYCAIFABgIAgUAGAgCBQAYCAIFABgIAgUAGAgCBQAYCAIFABgIAgUAGAgCBQAYCAIFABgIAgUAGAgCBQAYCAIFABgIBsS6ElU51b8+lq099vw4aczGXv24t1n4TP7Mb+TfBx9V3r8XlLY2UufL986iqIrXncMANrYdoE+fm/9BsoCXbC/AWv3YoBA5/dEPV/kbyJQgI2y3QJdiKfDBmoC3Ui7tw/9BVquaS9fGYECbJQNXwNdHPdnHvjbXDcDL0LPLc/a54+vL4xz3t8++KC3QFN/7t99uvjyyUfZX6MEBAqwUcwJdGGro8k1QfsKdFF3tHezePVy8Sr7VBEowEYxKNC0+TYtz/QVaOVPwLIgBAqwUSwKNNHVtDzTU6C1vwDHUVYhAgXYKKoCLfx3lPz38qOFFt6+m7z/+L1o8eVqdE76rb1LX9Q3JruEnrzXuFS7QOubnn92ISp2JK5v+FheQF0Kq7KZxUK35vdm0d7b/7dU/JFLk5lAn7yXbOPz0kfTuN5xVPkzdPreT36/3J90X85+sKr48UfpcKcPnlZ3LgmYPzpYpF5+JuXr+LwBoMJkBHqcdyhfWQ3QyY/n1YCdy8+qG1sJNHFQ41Jtp/ANm/5T0b1drFLZ8GKflxUU+1/bTOKoo2zXhJfkqmVSgRafwKV0Gy3rJUs394IlKz2alcc2vbxe6asXO7f6rthR5+cNABWmItBfL4fk3DwqDzZaCayqDSnQpFul4FzluK92It1p2fRxdTO1DYvm4En2Vn0zi60c5GuI8BNn73+yhRvlmJb1kl1qPlM/KnKb9z+rWuyc+O6Zf5lv1P15A0CFiQh0QXLG+Dg5+Yz2bj6L558Wx2+itMuLs89XScOo4o3Vie5RvoUn1+tLlQT6Kj1DX3qusunEyft381bYlcYNC53lLq1vJtXw95/Fr56KoaotFzozaZ37Im8RXonjtvWSnWze0PKDTDdzK38r+Tzj+Wez+s6lKyTtzKzZuvy26/MGgBKTEWh2qJ4s20nJm4n2kjZSbouT6qW/lUDFWflRban6QPr9B/na1U0fLduveb9MfcMraeen1g2bORbuOS6+bOlASwV6vrz/7vVOnMOwVh9k0Uot71xam9i5pLpsNFQyFKphBZ89fgC7x1QEmr8rzk7z41122tQ6YZYyW4lPGmAVWubS0pGVTYt18003bHhptnz9hj08Fopbtn+P3afEiUCLz6WIca/XKtBiM0fZWiel/S8EWqwuNn2SC7T18waAElMR6PnlO8Uxmy8qD+KT6jnlshdHnuUeua6VZpz9qbhzvLLpk+oF1KYNF2Yrvtewh8diO8W5eNtQpdLAqqPyOXx9vcUOuAValF7XddEXtdo5ueniT1fr5w0AJaYi0Cvld5aLyi6NerdGsXhp0E/Ngsu2XHJhde8DYbbqpmvdPE0bLsyWb7ZpD0v2ylusbUNgS5I8Xl38bV6v9RqouHIgSpn/5Z8/nq2uDaz+XJXGSl1prgYAHGybQMtq7CnQxfdmTeOTlpuuNdsaN7xqZl5p3kx5O/mp83FLc640KUphced6rb3wdYE++ehCsW91ga5+Gs0CrY5oAACBMYGml/rECewggWZmk6fYrQLN7hNonTiq9M3lyq71GsaBHr/9wRdxo0BXw0ARKIBvtkGg7o6MRoHWLFgaxnS8PC9t2HS7QMtmKxqKTXtY3k7aYq2O5i/ReArvXq92J1JxDbUu0Hyg58HFn/7+v1/reApPxxFAR6Yu0Pbbuzt3Ionp7JImlmvyN9l4TT3TvOHUbEfuzVQEmrZY287gGzuRWtar3Qtf9MvXBXqcDwONS51I51exRXUNDgaANUxeoMdlpzX3wsulGocxiUbcadGZ0rBpsW4ul8YNL7537r8vT38b9rDSkl20WP9T69TPSVixDdHH7lzvKCrVmOzZ+fz9ikDFOye1U3jHMCb35w0AJSYvUNHaqg/f6TyQXp4FL0/iGza9GvaZ66Z5w8fpvadiNHplMxWBLrb1t9faLiemzeIryy/FPZzN69XnA10Ohy0LVDRtT+u98GIg/etiIH3r5w0AJSYv0LS1tf95fi9ipe9k5YfOt3LKk/j6ppe3cn5abKZxw+nI0qVc6pupCDTrmWlpzGULJDdQPr4elU+rm9dLH5GSza/kmJF+1QJNT+HTGzNrAl3eypnGrgp2fd4AUGL6ApX9wtWBlPn3bvWaTGTVFmvY9PrJRFKOovJQ+cpmqp1Rx9K38u4msYXVZCJXHOtJHpXvrrqy3HJFoJVnJ1Wbx7K6hslEuJMToJXpCzSef1Qcz/u1GSpPlgf+6+VwnbXT2a1O4hs2vTTT8tJn04ZPInkRsraZqkBPS425ZoEup7O74lqvhByetC/awtVhTMu/CHv/6s7yPtPz9c1c+vfLiwgtnzcASLZAoMlZamI1OcfxkkfJIPHvp1+mj0k/22FCZdkTX990fULlhg1Xp+isbKYq0PLgIIdA03mky9MYtw4qevnrg5bnwi93IZsd+e27y1GulXuUkgmVkz0X93C2fN4AINiwQI1S8e3rax3Hp7unYPYOA0AB+oNAN0HlHvvTWcfOGfcUzH4oT3tPlztATxDoBqjejnncsa3XevunD47lyCxu2wToCwINTzK9vZTT6aybqqrr+SfpC0uGLGVjoRg0D9ATBBqYfByRbHIeX+pgxYb1ArB8/lREAxSgPwg0MNlIy/6Nu6Hr9WM1ZIlHcAL0B4EGJjkPP3tz/XK+1uvLy3S20NU0/QDQHQQKADAQBAoAMBAECgAwEAQKADAQBAoAMBAECgAwEAQKADAQBAoAMBAECgAwEAQKADAQBAoAMBAECgAwEAQKADAQBAoAMBAECgAwEAQKADCQTQv0ePkUs5PlXOghpl0/mtYTKia2OwDghQ0L9HQmHwM5ZYE++hsvxss3M3h3PO0GAIRgswKd31k9R/do0gL11GQsNjN0c7RcAabMZgWaSDMX6MKl5zea3Q/PAlXeDQAIwkYFehKd+/eFQF9fC/3I3lEgUABYyyYF+vra3m+PCoGeRHu/3WB2XxAoAKxlgwJdnLTfipcCXXXHV3n50SyK9i6lz9k9iXKDHEeLM/5Fq/XK/LPFd/fvNi2cJjy+HkVnf5ubJ71OkLyzlzz1/PF7URRd+qJpzWzLFxarfvAsXnZwXSkWi96+G1doW71gtZmeu1Nbv/n7suD6Eunrtxs/qSNxISX9fJs/xv9rll+fXtQ35estAFpsUKDHyUG4PHQXSvkf92bCIKvFci5ni6Vn+lnv/eI4/v6d7Jt5w6y88OLIvxFll1mXxvrf8s6qMw/uZV/kDd/ymost/+317PX+A2muR8Vi+w8a93LvZw2rVxaSAu22O7X1K3kFouDaFo6bX6YbOIny6yens9LWKx/j/3Mt/5wXi035eguAFpsT6OksOcwLgS4O0rMz0cBacpw59dW97GheqGmxQtJ7fyt9sfju03h+L8paRJWFk8UWQnp5Vxhr8Z1n6Qpn0zU/zd1bj1mo5Vn88k6+P/m588Ic5xaLJaudr+zl/udx/OR6ZqLa6gXlXvjOu1Nbv5JXIAqubmHxerHnSdKV+gaWLcrjVODOj/Eo97v7dAHANBsTaN5pVAg0dWFUN+jCWLmpsmM7O3c/zvyVrJR9Nzuyqwvnns0WKIx1Ja6smexAdc3k++maxTntUSG2ZYNZKuR10TRbLJ68X1u9oC7QTrtTXb+aV7AquKGgdI3VDpY2cLT87uJ/98eYt1QnPmICQI2NCfSoaNllAlgctPt3nyUX36KSElZtnfyoTY74/zNfJmuOxnHRhKouLPSyNNZSgMtz5azNVV5zKZhiB5cCbezqWr2dnQPXVl9VXRFot92prl/NK1htsLqFk5UDs4DyBvIuvOzU3P0x5i1VzuABmtmUQE+WJqmeDB7Js1Lpj9VZdJQvIvoykm/WFhbtv6WxqmJL1VFbc7Xl45JAT5Kulc9r1ayqyFasrV6poe/uVNev5omPa9mwLG+hth+1Hb5V7G37x5hdXuUMHqCJDQn0dJa3gOoCLSlBntkXTdOkh+N88d2i+ZVsp7awUIHo9i7npsaqrbnaclmgcdbVc/aDp3KXhWGyL2urF9QE2m13KuvX8sSOLD+Z8hbKHq5v4Gjl/baPMW3IcgYP4GBDAj2Wx2j5cCwdnk0qSZqgt4rv3lpucDMCzQYbRXLkVEN7L7xAm1uoPQRa2cBJstDJ8vqv62NMFcsZPICD6Qm02thJu64zG9RboOeri3YWaHlNt0AXPPn4QrLXpQsNvlugjiZejxbo+YYVnTuc3tSQL9T2MaaLnHAGD9CMlkBXp/KlbpFqL3a26o1V5/VKoOk10PLCnQVaW7NVoHG8Gm9U2drqLHiMQBuqLq/fcg30fPFFeQtyqPz5pg0cRVfyvW77GNP9O+IMHqCZTc8HWhzJx8XopcXRKju6l93TxXGdjMT8H/kyq87u7NivLtxZoLU1HQItbVCY8XjZHj2J8l74MQKtV11Zv5pXIDdY3oIcKn+raQMn0blH+TotH2N678K0Zy0AUERLoMmFt8tPs5Hd0hiJL7OXuQLSozt/N1npSr6ZfBxoaeHuAq2u6WqBLof/lMXWMA50lEBrVVfWbxkHer75c1uucdQ4DjR558y/zFdu+RiT9c/OOIMHaEZLoOKkvjzS8jjrrynOmU+yK6ZHRUMvir7/LB08eqVh4e4Cra5ZM2BizvnT5P29m8lw1TtlsYkbe5L1nALNN9N3d2rrV/IKRMHVLZw470S6UuzCsiT3x5g9OIAzeIBm1AS6ujm7MlJ9adZcbMUQ+qyz5WL5/s/Swn0EWlmzZsCT/Frt6axYrnbHabb3xb3wzQItNtNzd2rru++FX8qtuoW2e+HzTS931fkxipusAKCGnkCzGYDK0xfl7yed3tk0QkfipsJsNqak9RnJKYdWC/cSaHnNugEfLXbufLKJzw4Wi529XBoIWuz9Xv62U6DFZnruTm39Sl5BSXXVLTTOxrTagOiRc3+MceknBgBltuqpnKVjHjbDMWfwAC4QKLQyv8MZPIALBAqtnNIHD+AEgUIbL+/wkQM4QaDg5jiK6EICcINAwc1JFO1XH7kCAEu2SqAAAFMCgQIADASBAgAMBIECAAwEgQIADASBAgAMBIECAAwEgQIADASBAgAMBIECAAwEgQIADASBAgAMBIECAAxkQwL9HgCYYDNGmQqbEugGMr7eQIYzWzVcM1sznMpVoluzEWgQEGjIcM1sqxqxWzkClSBQP9kIVCVdM9tq5QhUgkD9ZCNQlXTNbKuVI1AJAvWTjUBV0jWzrVaOQCUI1E82AlVJ18y2WjkClSBQP9kIVCVdM9tq5QhUgkD9ZCNQlXTNbKuVI1AJAvWTjUBV0jWzrVaOQCUI1E82AlVJ18y2WjkClSBQP9kIVCVdM9tq5QhUgkD9ZCNQlXTNbKuVI1AJAvWTjUBV0jWzrVaOQCUI1E82AlVJ18y2WjkClSBQP9kIVCVdM9tq5QhUgkD9ZCNQlXTNbKuVI1AJAvWTjUBV0jWzrVaOQCUI1E82AlVJ18y2WjkClQwS6Js/3D48/Bf/kL/43eLFz79seCFAoCHDNbOtasRu5QhUMkSg3354mPL3yYvvshc/+GPthQSBhgzXzLaqEbuVI1DJAIG++eTwh1/Gb/7b4Tv/tHj18PDdL+NvPzl896/VFxIEGjJcM9uqRuxWjkAlAwT6Im9hfnX4ozj+5nb64rsPE5uWXpRAoCHDNbOtasRu5QhU0l+giwboL1evUoum//+i8qIEAg0ZrpltVSN2K0egkv4C/e5DeYnzYW7TF4k7Sy9KINCQ4ZrZVjVit3IEKukv0G9uv/vX//mvDw9/+J/jpDman60n75ZeZAt/r+BrANh9EOg6Fnb8XdYL/wsECrDrXE3ovDQCXceLZADTX+M3f0h64YUzf/DH0ovySpzChwzXzLZ6Imul8qsFq2hO4QWDBJp1ET08/NHaFmgBAg0ZrpltQyNN2RYqvyopohGoYMgpvMuZCFQpXDPbgkaasw1UfvVqg0ERqGSIQPPz8/QLeuGzbASqkq6ZbaByBLqWIeNA83bmi+SGo2LIZz4OVLwogUBDhmtmG9CII3v3K79aJYtGoIIBdyI9zNuXDxNNcidSlo1AVdI1s3e/cgS6ngEC/eZ2Mt9S1guf3xif3/5eelECgYYM18zefY24sne+8po/M4MiUMmQ2Zhe3E6Hgb6TXvD8Vk7A9C2zMWmEa2bvvEac2TtfOQLtwKD5QL9NZv38N18WLxbK/PlfG14IEGjIcM3sndeIM3vnK0egHWBGej/ZCFQlXTN75ytHoB1AoH6yEahKumb2zleOQDuAQP1kI1CVdM3s3a+cXvj1IFA/2QhUJV0ze/crR6DrQaB+shGoSrpmtoHKm/yJQEsgUD/ZCFQlXTPbQOUIdC0I1E82AlVJ18y2UHmDPxFoCQTqJxuBqqRrZtuovKpPBFoGgfrJRqAq6ZrZViov6xOBlkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKkGgfrIRqEq6ZrbVyhGoxC3QV/d/fePGjZ/ef+ojBoGGDNfMtqoRu5UjUIlDoE/ei1Zc+nx0DAINGa6ZbVUjditHoJJGgT6aRWXO3hwZg0BDhmtmW9WI3coRqKRBoI+vL5S5/8Hn2bn7qz//+kLy+u6oGAQaMlwz26pG7FaOQCU1gc7vJbZ8Vn7v0cKhl59VF+0BAg0ZrpltVSN2K0egkqpAX9+J9psueS6apeceDI9BoCHDNbOtasRu5QhUUhPoj12XOx//72ME+jUA7D7WBRoIWqAhwzWzrbbD7FZOC1SCQP1kI1CVdM1sq5UjUAkC9ZONQFXSNbOtVo5AJWsEOv/z/d/7iEGgIcM1s61qxG7lCFTiFOjL//Asjl+nQ0J/Oz4GgYYM18y2qhG7lSNQiUugx9GZB3F8lN6HdGZE93sOAg0ZrpltVSN2K0egEodAT1Jtns6ic89eXouujI5BoCHDNbOtasRu5QhU4hDo0cKciUb3fpv8e27MTUgpCDRkuGa2VY3YrRyBSpoFOr+TmDPX6Otr48/hEWjIcM1sqxqxWzkClTQLNHPm62vR+RiBdspGoCrpmtlWK0egkjaBns6iWzEC7ZSNQFXSNbOtVo5AJW2n8MfpJVCugXbJRqAq6ZrZVitHoBJnJ9L5pPs9MSe98F2yEahKuma21coRqMQ9jCnhVjz/KMraoeNAoCHDNbOtasRu5QhU4h5Iv+B82pG0d2t8DAINGa6ZbVUjditHoBL3rZwf/yR5isfrH1/6wkMMAg0ZrpltVSN2K0egEmZj8pONQFXSNbOtVo5AJQjUTzYCVUnXzLZaOQKV1AT66i91no6OQaAhwzWzrWrEbuUIVFJ7JtK1qA4D6ddmI1CVdM1sq5UjUAkC9ZONQFXSNbOtVo5AJVWBzv98P+XTKNr7yT/ev//rC9HeB7/nTqR12QhUJV0z22rlCFTi6ER6vbr96BETKnfIRqAq6ZrZVitHoBLnrZyr2zeP0zmZxoFAQ4ZrZlvViN3KEaikdT7QjNMZk4mszUagKuma2VYrR6CStunsml4MBIGGDNfMtqoRu5UjUIlLoKUWKAJdm41AVdI1s61WjkAl7unsGr8eCgINGa6ZbVUjditHoBL3dHaXswuf83tRNH46JgQaMlwz26pG7FaOQCWue+GTJ8K/fePGjQuL/y+Pj0GgIcM1s61qxG7lCFTiEmjS8MwZPx89Ag0brpltVSN2K0egEvdsTK8+S1qfZz8YP5NIjEDDhmtmW9WI3coRqITp7PxkI1CVdM1sq5UjUAkC9ZONQFXSNbOtVo5AJW0CZT7Q7tkIVCVdM9tq5QhU4hLoy4+Yzq5PNgJVSdfMtlo5ApW4Z2NCoH2yEahKuma21coRqMQh0OMo2v/p/QLmA12bjUBV0jWzrVaOQCWu2Zg83L4pQaAhwzWzrWrEbuUIVNJhMhEfINCQ4ZrZVjVit3IEKukwnZ0PEGjIcM1sqxqxWzkClXSYUNkHCDRkuGa2VY3YrRyBSpydSD7ugF+BQEOGa2Zb1YjdyhGoxCHQRRP0ps8YBBoyXDPbqkbsVo5AJY5T+I/fj6K9izdyfsIwpnXZCFQlXTPbauUIVOLqRIoYSN8rG4GqpGtmW60cgUoQqJ9sBKqSrplttXIEKmE2Jj/ZCFQlXTPbauUIVIJA/WQjUJV0zWyrlSNQCQL1k41AVdI1s61WjkAlbY/0OIiivQMe6dEpG4GqpGtmW60cgUqcAj3moXJ9shGoSrpmttXKEajEJdDEn2cv3nj/gh+DItCQ4ZrZVjVit3IEKnEI9HQWnfsi/erlncjDffEINGS4ZrZVjditHIFKHAI9is4Vdx95mRsUgYYM18y2qhG7lSNQSYfZmE5n57iVc102AlVJ18y2WjkClXSYD9TH5KAINGS4ZrZVjditHIFKEKifbASqkq6ZbbVyBCpxPhPp1vLFScQp/NpsBKqSrplttXIEKqETyU82AlVJ18y2WjkClbiHMe1/nn715DrDmDpkI1CVdM1sq5UjUEnbQPro4ODA061ICDRkuGa2VY3YrRyBSpy3cj6a5Xdyenm2BwINGa6ZbVUjditHoBL3ZCLzx+8vWqAX747uQEpAoCHDNbOtasRu5QhUwnR2frIRqEq6ZrbVyhGoBIH6yUagKuma2VYrR6CSdQL9i58YBBoyXDPbqkbsVo5AJU6Bzj97+0H6dLlLX3iIQaAhwzWzrWrEbuUIVOIS6MksfRZn8njOvVuOZXqAQEOGa2Zb1YjdyhGoxD2QPrsX6c8fzRhI3yEbgaqka2ZbrRyBSpy3cu4XZ+7cytklG4GqpGtmW60cgUpcszGV5gNlNqa12QhUJV0z22rlCFTCdHZ+shGoSrpmttXKEaiEFqifbASqkq6ZbbVyBCpxXgM93/j1UBBoyHDNbKsasVs5ApU4BHoSRZeepl+9uhdF48cxIdCQ4ZrZVjVit3IEKnGNAz1K5mE6ODhI5mQa3wBFoEHDNbOtasRu5QhU4hLo/NOomM7uZx5iEGjIcM1sqxqxW6EH12MAACAASURBVDkClaydzu43TGfXJRuBqqRrZlutHIFKmI3JTzYCVUnXzLZaOQKVtAp0/tRXDAINGa6ZbVUjditHoBK3QB+/F0VnHrz+8Qc+zuERaMhwzWyrGrFbOQKVODuR7iUdSAuBXov2Rw+jR6BhwzWzrWrEbuUIVNIyjGn/72ZnHsx/Fa0eET8cBBoyXDPbqkbsVo5AJe6B9Dfzm+AfzRhIvz4bgaqka2ZbrRyBSpy3cl5ZziJyzK2c67MRqEq6ZrbVyhGopFmg8zvJZCK5QJlMpEM2AlVJ18y2WjkClbRNZ5cLlOnsOmQjUJV0zWyrlSNQCQL1k41AVdI1s61WjkAlrlP4pOMoN+eJh254BBoyXDPbqkbsVo5AJY5OpLTjKBMoz0Tqko1AVdI1s61WjkAl7qdyXn6WCvTldZ7K2SEbgaqka2ZbrRyBSlwD6Y+jKDqY7V28sPj/yvgYBBoyXDPbqkbsVo5AJc574f80KyYE9eBPBBo0XDPbqkbsVo5AJe7JRF59drCw59lLXziX6AECDRmumT0BjVxN2Hz2BCrXiUagAuYD9ZONQFXS41yfCgrVr1wrGoEKEKifbASqkr7S58YVql25XjQCFawR6PzP93/vIwaBhgzXzNbVyNWragZFoI0g0JyX/+FZHL++HkXR/vhRTAg0aLhmNgJVAYFOBPcwpmQQ/VGUTas8OgaBhgzXzFbVyNUqG8xGoI0g0JSTVJuns+jcs5fXPAxkQqAhwzWzEagKCHQiOOcDTe5/P0lvQuJe+A7ZCFSBmj83aVAE2ggCTcjmA801ymxMHbIRqAIIVCUagQrap7NLpxFBoB2yEagCCFQlGoEK2gR6mj0NCYF2yEagCiBQlWgEKmg7hT/O5mHiGmiHbASqAAJViUagAmcn0vmk+z0xJ73w7aTHLQJVgV54jWgEKnAPY0q4Fc8/ipgPtAWNQ7cKAkWgm4xGoIK2+UCzWemjvfGPhd9VgWodvGXsCpQ7kTSiEajAfSvnxz+5u/jv9Y+9zGe3mwJVPHwlCBSBbjIagQqYjWkECFRdI3o/AO3K9aIRqGCwQL+5/e5f0y/e/O724eHPv4zrLwQ7KVDNK3ASywJlPtDNRyNQQVWgc/fsdX+Wg5nefHKYCfS7Dw8TfvDH2gsJAg2IbYEyI/2moxGooCrQ19f2m695vrxeGk7/1WEu0IeH734Zf5vrtPRCsosCrflTy6DWBaqUbbVyBCqpncIfR1FDt9GT6+WHy31zOxfoN7fT5uZ3H77zT5UXJRBoQNCISrbVyhGopH4N9OXClWc/eCrf+fWsMq3y4gT+32XXQL86/FH6zleHv6i8KIFAA4JGVLKtVo5AJU2dSI/SJxq/feMf79+//883kmdzRns3S3dzPjz8Ud6J9PDwl+k7LxJ3ll6UQKABQSMq2VYrR6CSxl74+aPlQ+FTzpb1uRDku3/NBPrmk/xsPXlZepEt+b2Cr3ePBoFq7xKAMgg04+WvD3J7HpRO5xPSa5wItG5Q7R0C0AaBCv7yl780vf0wucRZE+gP/lh6UV5lF0/hGcYUGz6RtVs5p/CSAQPpv0r737u1QAt2UqDciWRYI3YrR6CS/gL95naqSQSKQGPDGrFbOQKV9BfoV4dLFifqhnvhY2ZjMqwRu5UjUMlYgRZDPvNxoOJFiR0VKPOBmtWI3coRqGTsZCKG70TKyfvfdcJT0IhKttXKEahkrEDffHL4w+Xt76UXJXZYoFk2AlVJ18y2WjkClYyezu5bOQHTt6ZmY5LZCFQlXTPbauUIVDJ+PtBvf7dQ5s/zJmfphQCBhgzXzLaqEbuVI1AJM9L7yUagKuma2VYrR6ASBOonG4GqpGtmW60cgUqaBfr6x5erN8CPA4GGDNfMtqoRu5UjUIlDoNeSOUGfNX5vEAg0ZLhmtlWN2K0cgUqaBTq/l07EtH/Xl0MRaMhwzWyrGrFbOQKVOK+BPnkvdejbd73EINCQ4ZrZVjVit3IEKmnrRHqcObThEUm9QaAhwzWzrWrEbuUIVNLeCz9/fD19oEdtTuW+INCQ4ZrZVjVit3IEKlk7jOnVR9kTksY1QxFoyHDNbKsasVs5ApWsaYF+dmH5YKTLY2IQaMhwzWyrGrFbOQKVtAi0sGfSF//qXvm58H1BoCHDNbOtasRu5QhU4hJofvVzNRr0JDo3YkwTAg0ZrpltVSN2K0egEsc40DtRtfPo9bUzD4bHINCQ4ZrZVjVit3IEKnHfibRXHr70+hot0JZsBKqSrplttXIEKnEJ9NLnlbfmvx8zlAmBhgzXzLaqEbuVI1AJszH5yUagKuma2VYrR6ASh0CPfNx+JECgIcM1s61qxG7lCFTivAZ6y2sMAg0ZrpltVSN2K0egEpdAx3S5N4BAQ4ZrZlvViN3KEajENYxp77deYxBoyHDNbKsasVs5ApU4roEeR+e8XgRFoCHDNbOtasRu5QhU4hDoq0+j6OzFGzk/GT2vMgINGa6ZbVUjditHoBL3QHrB+AuiCDRkuGa2VY3YrRyBShCon2wEqpKumW21cgQqYSC9n2wEqpKumW21cgQqQaB+shGoSrpmttXKEaikVaBzbw+HR6AhwzWzrWrEbuU+Bfr6WmWa4aMus2aezkZNTtw97Xj91Uu3QJNHyp158PrHXh4Pj0BDhmtmW9WI3coRqMQ5ofK9rPdoUeG+h5uSEGjIcM1sqxqxWzkClbgEehRF+383O/Ng/qto1FT0OQg0ZLhmtlWN2K18hwS6juECPYmim/kd8Y9mHiYWQaAhwzWzrWrEbuUIVOKazi7Zw3xKkePo/Og9QaAhwzWzrWrEbuUIVNI2mUgu0NMZA+nXZiNQlXTNbKuV+xbo/N4sit6+m72xFOjLj2alxwqlr/OlCoEuzpMbOmjSbX42Sx4nXFq52Nj8TnQreWTm2d+605Ie9L3LzwYLNFNnLlAfc9sh0JDhmtlWNWK3cs8CvZTf9ZiprFDacX4T5N7PsgWL15eTF7lAT2aNHdyLbX7/jtzmcuVs7YVAb+T3VzrS8mdqRmc+QqAbAoHqpGtmW63cs0AXWnuWDvpJLxTmSlsYbf/zOH6yaCimHTCL1+e+SJdKzJkJ9LTZn9k2Lz1dbXOxctK2fHUvM2iix8UJ9su7zrSj5T4NP4VPtpSbc9wT4TMQaMhwzWyrGrFbuW+BZpczj6N0CuJMaYu3M+csRJQ15bLX+ctUoC5/ptvMem2O0m0uFsw7cbKMRKB5t3hz2vIS6/FggWYdR5lAF1ulE2ltNgJVSdfMtlq5Z4Hmilp8lWgmU1pu07iQ2UmhvJNCiVec/qxtc3UhM1NZrsnYmXYk1h8q0MWWLj9LBfryeuRhdnoEGjJcM9uqRuxW7lmgRessk1nxbyGuqgNTFna65HZbZZuyAZhuffFGcUrdmCZWOBo+kD65rHow27t4IYp8DBlAoCHDNbOtasRu5YGGMWWyqiou+7IyuGkh0Mjtpco2y1NzLjJKgmxIEwIecy/8n2ZFpo8hVwg0ZLhmtlWN2K3cs0CLu3SOSwItNxobBLo/c8mtss0uAi2lCQGPmkzk1WcHi8Czfh4Qj0BDhmtmW9WI3con0QI99+w4cvTN1Fug5eU21AL1CwINGa6ZbVUjdisPJlD3NdDl60x+aV+P6Exv3abwYyy2UcoMcA3UMwg0ZLhmtlWN2K3cs0Bzu+XaK/rFCzeeRKVe+NN0Yo7lONBGvVW3ebTsBc9cWhNoNe1Y6HrwQHo/Z+5LEGjIcM1sqxqxW7nvcaCZvI66jAPNm4T5QE3HSfxqbOlyHGgu1MzDNYE2jAPNFhg+DjTZhz0vMynnINCQ4ZrZVjVit3LvdyJ9/1n88qNcevV7g4q73mt3IiW+axphWdvmcXZb/PzT7N7OmkBradmtS6PuRLqX9sG/fbfxuwNAoCHDNbOtasRu5Z4F+rfXxH3qve6FT7Vab+UttnlxVhpAtLwXvridqSLQWtq97OX+fxxxDTSZj6Q0QckoEGjIcM1sqxqxW3mY2Zg+z96ozI90eflUtqbZmJKb1uuDLJNtJq3PSE7ldGHVHmwQaC3tycjZmLKgR0lqdPamh1N5BBoyXDPbqkbsVj71p3LW5hgNSnsv/KvP0rbw3zCZyLpsBKqSrplttXIEKlk7jClpDDOd3dpsBKqSrplttXIEKlkj0PlnF7p0Ra0FgYYM18y2qhG7lSNQSZtA58nE934ugiLQkOGa2VY1YrfyaQn0JKpwayoC9doNj0BDhmtmW9WI3coRqMQh0HQYgHws00gQaMhwzWyrGrFb+bQEqo37TiSvtyIh0JDhmtlWNWK3cgQqcQr00uc+YxBoyHDNbKsasVs5ApU4buX8vcf74BMQaMhwzWyrGrFbOQKVMJ2dn2wEqpKumW21cgQqWTMj/d7BB0+dS/QAgYYM18y2qhG7lSNQiVOgyxlMeCZSl2wEqpKumW21cgQqaXsq59mLN97nqZzdshGoSrpmttXKEajE/Vz4c9kI+pfNs5b2BIGGDNfMtqoRu5UjUIlDoOIxePKpn4NBoCHDNbOtasRu5QhU4hjGdEe0Ok9nDdM+9wSBhgzXzLaqEbuVI1CJayC9mICp9GIgCDRkuGa2VY3YrRyBShCon2wEqpKumW21cgQqcZ3Ci2fWnzQ9uaknCDRkuGa2VY3YrRyBSuhE8pONQFXSNbOtVo5AJe5hTPvZbCJPrjOMqUM2AlVJ18y2WjkClbQNpI8ODg483YqEQEOGa2Zb1YjdyhGoxHkr56NZ8bD5mx5iEGjIcM1sqxqxWzkClbgnE5k/fn/RAr1418vEdgg0ZLhmtlWN2K0cgUqYzs5PNgJVSdfMtlo5ApUgUD/ZCFQlXTPbauUIVLJOoH/xE4NAQ4ZrZlvViN3KEajEKdD5Z28/yB6O5OO5xgg0ZLhmtlWN2K0cgUpcAj2ZRWcygUZ7txzL9ACBhgzXzLaqEbuVI1CJeyB9di/Snz+aMZC+QzYCVUnXzLZaOQKVOG/l3C/O3LmVs0s2AlVJ18y2WnlogT5PGLuRjeGajak0HyizMa3NRqAq6ZrZVisPK9DnBeM2szGYzs5PNgJVSdfMtlp5SIE+l3Ra4/W18efHY6AF6icbgaqka2ZbrTygQJ8/723QIw8XGMfgvAZ6vvHroSDQkOGa2VY1YrfyKQl0fhRNU6AnUXTpafrVq3tRNH4cEwINGa6ZbVUjdisPJ9DnVdat8Ph6NFGBLpqdUbR3cHAw87OHCDRkuGa2VY3YrXw6Aj2OosuPJirQ+adRMZ3dzzzEINCQ4ZrZVjVit3LfAq1ps4nGNY/37y7Olacp0OV0dr9hOrsu2QhUJV0z20rlV69Woicj0ITpCtQrCDRkuGa2FY3Us01UfjWhEo1ABa0CnT/1FYNAQ4ZrZpvQSGP27ld+NacSjUAFboE+fi+Kzjx4/eMPfJzDI9CQ4ZrZu68RV/ZuV35VUo5GoAJnJ9K9pANpIdBr0f7oYfQINGy4ZvZua6Qte4crv1qhHD2ZXviEqQr0KIr2/2525sH8V9HqEfHDQaAhwzWzd1gja7J3tfKqPStn8Ai0hHsg/c38JvhHMwbSr89GoCrpmtm7WHlNnlV7xtO6E2myAj1KHgafzyJyzK2c67MRqEq6ZvbuVd7BnjECLdMs0PmdZDKRXKBMJtIhG4GqpGtm71jlneSZRk9qNqZpCjRTZy5QprPrkI1AVdI1s3eq8q72jKc2HygC9YbZo8ls4VTuhx72jEMLtO+M9NMU6PxO0nGUm/PEQzc8Ag0Zrpm9Oxrpm70jlfezZxxeoNuFoxMp7TjKBMozkbpkI1CVdM3sXai884VPGY1ABe6ncl5+lgr05XWeytkhG4GqpGtmb33lQ+wZI9AyroH0x1EUHcz2Ll5Y/H9lfAwCDRmumb31Ghmcvd2Vdxuy1BiNQAXOe+H/NCsmBPXgTwQaNFwze7s1MiZ7iysfLM80GoEK3JOJvPrsYGHPs5e+cC7RAwQaMlwze4s1MjJ7Sysf3vQsohGogPlA/WQjUJV0zeytrHysPWMEWqaTQP9fhjGty0agKuma2dtXuQd7xgi0TAeBzu8xkH5tNgJVSdfM3rbKvdgzRqBlmgT6+L2Dg7fvFq9Or0cIdG02AlVJ18zeqsp92TNGoGXqAn15Pet8z24/SidWRqBrsxGoSrpm9vZU7tGeMQItUxPo62vF8KXEoKlN9z0MpP8aABSoXvgMHGdeoMkI+pvZf7eSG5Ki6LKHhyLRAg0Zrpk93XbYWwkBs6db+ZLRQ5Yao2mBCqoCnd/JR84fRdH5xJ8emp8xAg0brpk9VY28VRAse6qVF9Ts6UWfCLRMg0CzW98X8ty/7qf5GSPQsOGa2dPUyFuSQNnTrDwnkDzTaAQqqAr09bW8yyi9Frp301MMAg0Zrpk9SY289VZ4g06z8pSA9owRaJlWgXqYhikHgYYM18yepEZMCzSoPWMEWqZVoD6mEclAoCHDNbOnqJG3qgTJnmLlnkcsNUcjUEGbQMcP/1yCQEOGa2ZPUSNmBRrenjECLYNA/WQjUJX0xndr/gxi0MlVvhF7xgi0DAL1k41AVdIb37Uo0A2cui+jEagAgfrJRqAq6Y3vmhNo6G6jSjQCFSBQP9kIVCW98V1TAg07ZKkxGoEKGgRah8lE1mYjUJX0xnftCLRmz/D6RKBlEKifbASqkt78to1eeA15ptEIVFC7lfPjG3V+woz067IRqEp689u7L1CVpmcOApXwTCQ/2QhUJd3x/gb8qVi5pj1jBFoGgfrJRqAq6Y73d1iguvJMCC3QcD+yECBQP9kIVCXd9Y3w/lSpXN+ecWiBBv6peQeB+slGoCrp7m8FPxA3X/kU7BmHFWjPP3yP34uivUtfjEkcCwL1k41AVdLbvhm4HbPhyivyVPzYAwq056WXe9kgIX+Txg0AgfrJRqAq6ZrZmwyvNT0RaByfpPMVv7zj846f3iBQP9kIVCVdM3tj4U0n7jsp0H7Dz+Z3olvJ/6+vZf/rgED9ZCNQlXTN7M2EO657ItCFOPOW55HHiYt7g0D9ZCNQlXTN7A2Eu7uNdkigNW020bYBBOqHXT+a3OGa2Qg0EO1DlhDoktfXNHuRWgU6f+orBoGGDNfMRqAhqNmzOmYJgS45js73jfSIW6DJGKszD17/+AMfDzZGoCHDNbMRqHe6DJdHoAUn0xzGNL+XTcP0+lq072GQAAINGa6ZjUC9srbpWaSHCO/GtAR6MttT7IN3C/Qoivb/bnbmwfxXUXRufBsUgYYM18xGoB7paM94pwS6os+Je86xbvvTKdCTKLqZjxN4NPMwzAqBhgzXzEagvugszzTdc3gPpiTQe9r+dAk0HRmQD7TycZEWgYYM18xGoH7oY894RwXa91bO+ZGX64ujaBbo/E4i9lygpzNmpF+bjUBV0jWzPYb3tGeMQFOOfFxdHEmzQDN15gJdjvgfAQINGa6ZjUDH0t+e8a4KtN9sTMcT8CcC9ZSNQFXSNbO9hPe68CnTfYQPYyrzgYrHtykOBHWdwicdR7k5TzyIHoGGDNfMRqDDGWrPeIcF2n0awpNougLNOo4ygS5kSifS2mwEqpKumT0yvPuQpcb0ceFj4JlIEodAT2fR5WepQF9e9zFSAIGGDNfMRqBDGCXPNH1E+EgQqMQ1kP540TA+mO1dvLD438NcJwg0ZLhmNgLty7imZ5E+MNwDCFTivBf+T7PiAoOPuaIQaMhwzWwE2gsv9owR6GRwTyby6rODhT3P+nlkEwINGa6ZjUC748ueMQKdDMwH6icbgaqka2b3C6/Jc9xDNRHoRHAMY/r4J6uRS6fv/w3DmNZlI1CVdM3sPuF+7Rkj0MnQNpC+6cVAEGjIcM1sBLoW3/JM031sZGA0AhV0ECj3wnfIRqAq6ZrZHcMD2DNGoJOhJlBxg9QS7kRam41AVdI1s7uEh7FnjEAnQ70FelIXKPOBrs1GoCrpmtlrw4PZM0agk6Eu0Pl/uXHj/dnexRsFP/18fAwCDRmumY1Amwlx4VOme95en2gEKuhwDdQHCDRkuGY2Am0gsD1jBDoZOgxj8gECDRmumY1Aq3gfstSYHmKjHaMRqIDnwvvJRqAq6ZrZjeGbkGeaHmrDHaIRqIDnwvvJRqAq6ZrZtfCNND2L9IDbXheNQAU8F95PNgJVSdfMroRv0J4xAp0MPBfeTzYCVUnXzJbhm7VnjEAnA8+F95ONQFXSNbNX4Zu2Z4xAJwPPhfeTjUBV0jWz83AFe8YIdDLwXHg/2QhUJV0zOwnXsWeMQCcDjzX2k41AVdI1s7/e+IVPmb7JsEo0AhUgUD/ZCFQlXS15k0OWmkCgE4HnwvvJRqAq6TqxNXtuWp8IdDLwXHg/2QhUJV0hU1+eCQh0IvBceD/ZPsOfJ/QJ95jdF1sCnUDTMweBTgSeC+8n21/484Lu4d6y+2NIoNOxZ4xAJwPPhfeT7Sv8uaRruKfsIVgRaF2eViqvRwcWqPbfpn7wXHg/2Z7Cnz8fYNBdKHxg+mZiGpueJipvjA4q0Ck07/vAc+H9ZCNQlfRNhDhO3A1U7ogOKNCeV0geX4+iPS/TxQ0GgfrJ9hP+vEq3cC/Zw9h1jbiveu565e7ocALteY35OLvG6GO6uMFUBTr/+Ead8dPTI9BOINCe6YG333Y873blbdFTEejpbO9mnAwTGj/McjhVgTY91TjiTqS12V7Ca/7sZtDtL3xwesiNrzmYd7jyNdHBBNp4rdnNUda/7WOujuEgUD/ZCFQlPdiW1x/Ju1r5+uipCDTH+xMwe8E1UD/ZCFQlPcxmOx3HO1l5p2jPAq1ps4mW9U9nHmZ8HwwC9ZONQFXS/W+y86G7c5V3jp6WQL1M+D6cdoH+xVcMAu0CAu2b7nl7fQ7c3aq8T/SUBHoURXt3+yb6pP2pnIu9YyB9p2x64VXSfW6sz2ljvFOV94yekEDnvzqYRXs/6xvpEedTOe8su5Aue7jCgEA7gUB7pvvaUJ+mZ5G9G5UPiJ6QQBMeq57DOwSa+HPv4j/e/6/vzyIfw6wQaDeG+HMnCh+Y7mcz/e0Z70jlg6In1gvvZcLiwTjnA42+n+3U/NOIp3Kuz0agKuk+NjLooN2JygdGT02gqt3wzhnpV3MwHTGh8vpsZmNSSR+/iWH2jHeg8sHRE7kTKXtsRjxJgb6+JiZR5qmcHbKZD1QlfeT6g+0Zb3vlY6InItBly85HC28wbQ+Va3oxEATag3763KHC+6ePWXmMPeOtrnxk9FRmY0ofmxHP7/l4ZMZg2p4Ln+OjhYxAQ4ZrZm+pRgZe+JTZW1r5+OjJzAd6kg0T2pvgQPpj0So+5hro+mwEqpI+aK2BfRXV7C2s3E90UIH2mpH+5XveRqoPxT2MqRj+eexhLhEEGjRcM3vbNFKz59Cpz7eucm/RgQW6XThO4T9Oxn9e/OD+Pyf/vz1+VlAEGjJcM3urNOJLnmn2VlXuMxqBClydSJ4ntUOgIcM1s7dGI96ankX21lTuOxqBChCon2wEqpLeeUnP9oy3p/LRVD8uBCphOjs/2QhUJb3bYt7lmWZvQ+XjqX9oCFSCQP1kI1CV9C4LhbBnvBWVj6fpk0OgEgTqJxuBqqSvXSKQPePpV+6Dxk8PgUrcAv3z/SW/ZyD9umwEqpLe/u1w9ownXrknEOhaXAL904yHyvXJRqAq6S3fC3LhU2ZPtnJvVD/C7DNEoBKHQE94Kme/bASqku54v/nI95w9ycq9gkDX45zObu/uX5Y8HR2DQEOGa2ZPTyO1wz6EPidZuWfqH2T6SSJQiXMcqN8b9BFoyHDN7IlpZDPyTLMnVrl/EGgHOkxn5wMEGjJcM3tKGtmcPeOJVR4EBNoB1yk8Au2XjUBV0sXXG7VnPKXKQ4FAO+Cczo5T+F7ZCFQlvfhiw/JMs6dReUAQaAccAi0908MDCDRkuGa2vkY23fQssvUrD03zJ4tAJa5xoC+vRfs3CsZMZJeBQEOGa2Zra0THnvEEKg8PAl2PS6D3GAfaKxuBaqBmz1i7cu5Emgju58Ij0D7ZCHTzKNozRqAOEGhCOpDe57OWEWjIcM1srXBde8Y2BMpsTGvp8Fx4HyDQkOGa2Srh6vaMjQiU+UDXwUB6P9kIdGNoXvgUGBEoM9K3w0B6P9kIdCNU5allz9iQQGvRCFTg7ES64jUGgYYM18zedFOohJXKG9IVoxGowPlc+L2bPmMQaMhwzexNhTe2PU1U3pyuGI1ABc7nwkfR3kUG0nfORqAhcZ25737lznTFaAQq6PRYY8aBrs3eGoG+leAxewOFu6977uqPfP3lXQQ6ERCon+wtEehbBd6yQxfe2mm0mz/yLj1kCHQi8FROP9lbIdC3JJ6ywxa+pst9F3/k3QYZINCJgED9ZG+DQN96y79Bg5/ItrpkB3/krS1ukR4kvBMIVIJA/WQjUM90EskO/sgR6HbhFuirzw6iaO/gg4Ynyv2vf3t4+M7Pv8xevPnd7cPDxhcCBBoyvNtib1Xxkh2kcHe3USU9RHhHNlO5q3QEOhGcAl3Nx1QbUv+Hw5R3/il58d2H6Ysf/LH2QoJAQ4Z3W2xLBNpVIWm67/AeINBGEGhG4s+zF2+8f6Fu0BeH7/xDHH/7SebJh4fvfpm8ePev1RcSBBoyvNNSNX96Majnwmv+mOxgnjA/8nr907t4gUAlDoGezqJzX6RfvbwTlWdmevPJ4S+T/xetzcX/39xONfrdh0l7tPSiBAINGd5pqekLtJ8803R/4b1BoI0g0JSj6Fxx99H8TnRefuu7D/Mz9IeHv4jjrw5/lL74qvaiBAINGd5pqWkLtGfTs0j3Ez4IBNoIAk2Y3xGtztPZucZbOVOBPsyao4vz+h9VXpRAoCHDOy01YYEOs2e8gz9yBLptdJgP1DE5aHqi/uaT/Gz9m9vv/rX0IlvoewVfgzINAtXepZSaMbR3SJMGJoUAJwAAIABJREFUgWrvUk8QaEIXgabn6wh0e5igP7deF97Z+g8EgSbM70S3li9OoqZT+BfpMCbhzB/8sfSivDSn8CHDuy02uWFMQ0/cRfrw8NEwjKkRBJrS0omU8eL2O8n1znUt0AIEGjK843IB/Dmi8LHyTNOHhnuAO5EaQaApp7No//P0qyfXo4YHzH2VD6NHoHk2Au2HB3vGO/kjR6DbRdtA+ujg4KDxVqT4D4fFSE964bPsbRDoZGZj8mPPeDd/5N0+GwQ6EZy3cj6a5Xdy1p/t8ebh4Q+La5zFkM98HOgvSm+uQKAhw7sv6lmfQwr3Zs94V3/kXT4cBDoR3JOJzB+/v2iBXrxb70B6KG7V5E6kLHtLBKo9I72PC58yfewGxmQzI30TCHQtX8lb3d98cvjD5e3vpRclEGjIcM3s7uFdO5j7pI/fxPBszXAEOhX6CzSfcSkhudL5rZyA6VtmY9II18zuGF6zp5cnum9D5YHSFaMRqKBZoK/ySUBP366fwb84LAk0/vZ3i69+njc5Sy8ECDRkuGZ2l/Ag8kzTfW1oSDYCbQKBxvHL60XH+3FTH9IQEGjIcM3sdeFhmp5Fusdt9c5GoE0g0LT/PR86/2nzKKb+INCQ4ZrZ7eEh7RlPuvLQ6YrRCFRQF+jJwpn5IPo4nt9bvLpVW6Y3CHQNY+wy1cLDyjNN97/J7tkItAnzAk0eCS/bnMc+HguPQNsZ55hpFh7cnvGayn2O1mrKRqBNmBfoQpiluUPmd3ycxCPQFsaKZoKFb8KecVvlnoe7NmUj0CasCzTxZfmU/SRqnI2pHwjUzWjZTK3wDdkzdlbu+3ar5mwE2oR1gS7O4Ctzh5zOPJzDI1A3uyXQ8Bc+ZXrDe/7v93dkI9AmEGhVl/V3BoBAnVSNs82zYm7UnnG98hAznjqzEWgTCBSBDslGoKGHLDWml15tUp8I1IF1gc6rTzFOn3DMNdB12YPDa9rpL54pFL55eabp4uuN2jNGoA6sCzQ+qna6H0dNM9L3BIG62AGBKjQ9i/TlVxu2Z4xAHZgXaLXTnWFMnbLNClTPnvGy8s3bM0agDswLtDqQ/oiB9F2ybQpU1Z5xWvmmz9xX2Qi0CfMCTW/lvFy0QV9+5OdmeATqYmsFOn6/x7PZbqMyCLQRBJo9Dmn/N/fv3//sevLl+CugCLSF8R7SKHyC9tyoPhGoAwQax38qHoeUPhLpZz5iEKiTLRSovjzr9tz85QME2gQCjfMT90yfl596iUGgbkbbaMOFq9uzJs+3NHYFgTaCQDOe/PPHN3563489YwTaxlYJtLqzG9eIS54bNygCbQSBBgGBtjC2QbexwhtUv1mNtNoTgW4oGoEKEKif7N2fD7T5wucmNVLvNKru0yYNikAbQaBBQKBrGHP4b6Bwp6g2p5GmLncEqhGNQAUI1E+2anjg7bdpakOFN49Xqu3YJg2KQBtBoEFAoCHDQ258jaE2UbhztCcCVYlGoAIE6id7JwXaQU/BC28bLI9AVaIRqACB+sneQYF2clPYwuvdRq17iEA3Eo1ABQjUT/auCbSrmMIVXh8uv3YnEehGohGoAIH6yd4tgXa3UqDC18uzaT/phd9INAIVIFA/2Tsk0F5OClF4R3vGCFQnGoEK3AJ99dlBFO0dfODldk4EGjLc47b6Csl/4Z3tGdenc/a9L63ZCLQJBJpzvJyPycN0oAg0aLivDQ1oz/kuvIc9YwSqE41ABS6BJv48e/HG+xeYULlT9tYLdODZsNfCe8kzTR89j8BwEGgjCDQleRTnF+lXL2uP6RwCAg0ZPnoLNXt2dpG/wnvbM84q19EnAnWAQFOOVk+Wm9/hqZzrs7dYoKN6YvwU3ue6Zyk9/VdDnwjUAQJNmN8Rrc7TGc+FX5u9pQId3vQssscX3r3TvZ4+Onw4CLQRBJrw+pp4EmfpxUAQaMjwoSuOtWc8vvAOw+Xb0seFjwKBNoJAExBo3+wtFKiXHuxRhY+SZ5o+InwsCLQRBJowvxPdWr44iTiFX5u9dQL1NP5ncOHjmp5F+sBwHyDQRhBoCp1IPbO3S6Ce7BkPLdyHPNP0tSEDN9wlG4E2gUBTTmfR/ufpV0+uM4ypQ/YWCdSfPeNBhXtpe+bpHVIGb3xdNgJtAoFmpDciHRwceLoVCYGGDO+xrJcLnzK7b+F1fYZogfrxc3s2Am0CgeY8mhWPhr/pIQaBhgzvuJyHTvd6dr/Cm/U5WHKucF8t3NZsBNoEAi2YP35/0QK9eHd0B1ICAg0Z3mWh0SM+Hdk9CnfaE4H2T1eMRqACprPzkz1tgYaRZ5rdtfCK0vwY1BHu6wpBezYCbQKBJsw//smq4Xn6/t8wjGld9nQFGqjpWWR3KrzuMwQ6Ml0xGoEKGEjvJ3uiAg1rz7hT4U02q5/DD5Jcc7inja/LRqBNINCEkjNPZwh0bfYUBRpanml2e+EulSHQsemK0QhUUBPo62tRDe5EWps9NYEGb3oW2V2GYtY1hkDHpitGI1BBvQV6UhforYYV+4FAQ4bX3tmQPeOWwtsdhkDHpitGI1BBXaDz/3LjxvuzvYs3Cn76+fgYBBoyvPJ6U/JMsxsLXyswBDo2XTEagQo6XAP1wWQE2vNw6rr4hAS6QXvGTYV3s5cfxdELrxGNQAUdhjH5YCIC7XlEdV98KgLdrD3jeuFd1YVAR6YrRiNQgaWB9D2PqT6LT0KgG7dnXC28h7e8KI47kTSiEajAkEB7HlW9FtcX6CYvfMpsUXivzxeBjktXjEagAgTqZXFdgW6u072eXRTeW1k+FMdsTBrRCFRgR6BvVfG5uOLRtKkRn82khQ9s8I03HPOBakQjUAEC9bK41tGkKs+Er7/u97GWGWu41k89qD4RqAMEGgR9gdaE2KuPY93iGkeTbtMzo+dfJd+YHfqLQKcCAvWy+OaPJuwZI1CdaAQqQKBeFt/w0TRBeW7enjEC1YlGoIJWgc6f+opBoB6p2nPjB9Mk5JmAQDWiEajALdDH70XRmQevf/yBj3uSEKgvGtqemz2YJmPPGIHqRCNQgUug83vJNEwLgV6L9j3cFq8v0J3ohW8+c9/kwVT9VMYVPvb6AwLViEagApdAj6Jo/+9mZx7Mf+VjOlAE6gHnhc/NHUz1j2RM4eMv4SJQjWgEKnAI9CSKbuZzMj2a7ch8oD3PPHstHvpoau1z39DB1Ph5DC/cRy8YAtWIRqACh0CPoivLSe2Oo/OjYxDoCGr2rPhmEweT68MYXLiXcQQIVCMagQoc09nd2fvtUqA780ykXv6cymxMa+SZhofKLmj5W4JAldIVoxGooG1C5VygO/RUzh767Ld4oKOpgz3j0AfTWy36HF54t8rWgUA1ohGowJZAt2pG+s6OCXcwVeVZ/ywQqFK6YjQCFbhO4ZOOo9ycJzyVc3227/A+fglU+Fp5ptnDwmv+HGbQnfqR90tXjEagAkcnUtpxlAl0IdPd6EQKmu0zvK9bQhTeyZ4xAlVLV4xGoAKHQE9n0eVnqUBfXo+SDqWRINDO9BeL98K72jNGoGrpitEIVOAaSH8cRdHBbO/ihcX/V8bHINBuDLKK18LXX/YsZyNQnXTFaAQqcN4L/6dZlOPBnwi0EwOV4rHwXvJMsxGoTrpiNAIVuCcTefXZwcKeZy994SMGga5luE+8Fd7XnjG98GrpitEIVGBmPtDA2SPDR9nEU+H97RkjULV0xWgEKkCgfrLHhI91iY/CB9kz5k4ktXTFaAQqcAv0z/eX/J5xoOuyPXlkkEnGFj7gzH2VjUB10hWjEajAJdBVH1I6LejYGATahJ+elHGF9+42qmQzG5NOumI0AhW4p7NDoH2y+4f7kWcaPnjNfkOWGrOZD1QnXTEagQqct3Lu3f3LkvGPRkKgZfzZMx5c+Hh7xmM1Mrb0bfqRe05XjEagAtdkIh4mUZYgUIlPe8aDCu85XL4l26pG7FaOQCVtszF5BIEu8SvPNLzn8r7kmWZb1YjdyhGoxHUKj0D7ZXcN927PuGfhPu0ZG9aI3coRqMQ5GxOn8L2yO4WHsGfcq3C/9owNa8Ru5QhU4hDo62sepmASINAAp+7L8K4L+rZnbFgjditHoBLXONCX16L9GwU/YSD9uuw14Z67jSRdXehfnglmNWK3cgQqcQn0HuNAe2W3hHsdslSloxHD2DM2rBG7lSNQifMaKALtle0Kr9kzjD5btRjgzH2JWY3YrRyBSloG0o8+bxfYFGhQecbdzOi926iMWY3YrRyBSlzjQP32IRkUaNimZ8Y6N1bl6V2fhjVit3IEKmEgvZ/scvgm7LluSGd4eSaY1YjdyhGohIH0frJF+EbkmdCiyM3YMzasEbuVI1CJsxPJx5OQVlgR6Gaanhn1E/S3HN8Itw92NWK3cgQqcQh0fmfvps8YGwL1ZM/nzzst1ijQDVz2LGFWI3YrR6ASxyn8x+9H0d5FBtJ3zv7a261Gz58PF+hm5ZlgViN2K0egEud0dowD7ZX9tacT9+fPvQl08D70waxG7FaOQCUI1Ae+Lns+L+iysL49Y8MasVs5ApVs5VM5mx2j9Tu13p4dtfr8eS+Bug3aa/fHYVYjditHoJItFKjLMiq/Ux0ufHZtmfb1p0OgPQsYiVmN2K0cgUq2TqDPn7tEs/HfqU6d7l1P7nvrM24ZyLQ5zGrEbuUIVFIV6PzjpM998a9kQr3wz587DbrZ36maPZvt2LFrfoA9Nz9kqQmzGrFbOQKVVAX6+lrSZTTdTqRpCLQmz66zMTUuM6TxOQl7xoY1YrdyBCrZMoE+ryK+t6Hfqcamp+uXqkMz1XlJooUJnLvnmNWI3coRqGTLroGqC9RhxOEC7a9P/Z4jgVmN2K0cgUq2S6A1f0rrhP+dctvQ8UtVb61WDNq3+dmoTjSikm21cgQqQaCdaTPhMIH2bX062p1oRCXbauUIVNJ8DdQ7Wy/QNWfiAwTa+9zdedaORlSyrVaOQCUItAPrrmMOEGjfc/e2x8KhEZVsq5UjUMmmBPq1FxoE6mfDLVQNOGbdYvW+BZTtOaCInab3jyXgVsyDQKfcAlXohV/b+Myy+/TCuwpopnLm3rALtttha382nei/Ff3KtaJpgQoQ6Bq66LOXQMfY863G3bAs0E5/3tYyZCvaletFI1DBlgl083cidTusut+J1EOftSFLjn0xLNCG9v0ABm0FgTaCQBFomU6HVFeBdten054IVKQjUIVoBCpoEGid6dzKqTAbU6fjyf1L1azP9q3Vh8u3HuJ2BXq1yga3gkAbQaATF+jE5gMtslvC+zY+68Pl1xzhUy08PAhUIxqBChoEunqY3ASns0tpttB0PVLWZ7s/G4bL1w7w6m1QY/d+BKoaWffBBN0KAm0EgU77GmgLU/bIMH3mbyJQBwhUJRqBChCon+zW8I7Nz0Z7xgjUCQJViUagAgTqJ7slvJM9G87clyBQBwhUJRqBChCon2xX+BB7Vmf3RKAOEKhKNAIVIFA/2c3hXc7da0OW6ousOcAnWPiG8OFPeuH7RiNQAQL1k90Y3jLqP6eDPWME6gSBakQjUEHtqZx/vv/70YOW6lgU6PM6pe/Xh8s7N99+gE+t8A2m+/AndyL1jEaggu2akb6VaXmkQZ/SoN3lmYBAHekIVCEagQoQqJ/scnizPZcC7WfPhNYDfEKFbzqd2ZgUohGoAIH6yZbhTn1mBu1tz5SW43syhW8+PWY+0M1HI1ABAvWTvQov6bIu0EH2THEe3xMpXCM9/Xe8PodsZQqV60QjUAEC9ZNdhD9v9edgebaH+9rQkGyrGrFbOQKVIFA/2Vl49XLnBuwZT6JwrXTNbKuVI1AJAvWTnYbXeotc9vSpzykUrpaumW21cgQqQaB+sr9uvGuz2Z5+9alfuGK6ZrbVyhGoBIH6yf66bs84FWiP4fKDw71vsUe2VY3YrRyBShCoDxoanyk1ea5/Dmfsmi/aTa/CPTvcrEbsVo5AJQh0PNWuo5y6PTt5sVnFbfQo3P8IAKsasVs5ApUg0NE06rPRnh2kWOq377gDnQsPcCXBrEbsVo5AJQh0JA3Cq1/27KzE588HGLRr4SGuxZrViN3KEagEgY6iQXZOefZugCLQtema2VYrR6ASBDocYbrlL1WDpHro8HmVTvvRsfBau7jbamuyrWrEbuUIVIJAh1LyXP5L5RBUZxci0J7pmtlWK0egEgQ6kLLlkl+q8Xaq+bObQbsVXr8y68OgZjVit3IEKkGgg6g6zo+ZEGjfdM1sq5UjUAkCHUC7PYd7CYH2TdfMtlo5ApUg0N6U9ebTSgi0b7pmttXKEagEgfakJDfPSkKgfdM1s61WjkAlCLQXQm0hfDTIn/TC62RbrRyBShBoD1rs6SccgfZM18y2WjkClSDQzrjt6e9oGuJP7kTSybZaOQKVINCOOPWZZSNQDRCoRjQCFSDQTrj0ucz2FT7An8zGpJNttXIEKkGgHWi2ZynbX3hffTIfqFK21coRqASBrmO9PWPPR1M/ffYs3Ks+DWvEbuUIVIJA22k/c19lqx7KmtlWNWK3cgQqQaCtuLuNKtkIVCVdM9tq5QhUgkBb6D5cHoHqpGtmW60cgUoQqJPGAZ+ubASqkq6ZbbVyBCpBoA562DNGoFrpmtlWK0egEgSaUjVkL3vGCFQrXTPbauUIVIJA4+royI6XPcvZCFQlXTPbauUIVIJAK/fn9Jdnmo1AVdI1s61WjkAlCLTe4OxrzxiBaqVrZlutHIFKEOh4e8YIVCtdM9tq5QhUYl6gDnv2uZUyRqBa6ZrZVitHoBIE2mTPnvpEoFrpmtlWK0egEusC9aJPBKqVrplttXIEKjEu0AZ9DpqqCIHqpGtmW60cgUosC7Sp8dmz82iZjUBV0jWzrVaOQCVmBdpkz+zu90HZCFQlXTPbauUIVGJToI3yLCYPGZSNQFXSNbOtVo5AJfYE2tz0XM1cV1rYMTl8bcFNHE1XE5q+ETJ73Z8UsxqxWzkClVgTqEOeYu4lsbDj8UQNi4Y/mq4W1L8VLrvpQ6lkW9WI3coRqMSWQB32lFPXrRZ2PCCztIVlduBf6KuS6jdDZTdWWs22qhG7lSNQiSWBOuxZnjh5ubTjEe0VBxfZYX+hr15tM2ig7OZKq9lWNWK3cgQqMSNQpz1dDS0EikBbs61WjkAlJgRaVUHVi02SeF6lcVPFKmGPpqtVyt8Ok+2otIJZjditHIFKdl+gNQ80XdmsGwKBItD2bKuVI1DJjgu0boHGjqE6NX9mi9e2l3sl6NFU82fFoEGyXZVWMKsRu5UjUMkuC7TBAM0d6w0gUAS6Jttq5QhUsrsCbTj8u9ozRqAIdG221coRqMSGQNM3eugTgSLQtdlWK0egEgMCzV52PnmvL45AEWhDttXKEahk2gLtqruU6s+1dND302dMLzy98OuyrVaOQCVTFmhP5dUFWt9Sdx0jUATanm21cgQqma5AG+zVjvPn2t+etXzuROJOpGq21coRqGSyAnX4q4Xmn+uAxmfrDiDQcrZVjditHIFKdlygfVuxHXag0SrMxqQCAtWIRqCCqQrUcQmyjfrPdYQ+23agQSrMB6oCAtWIRqCCHRZo3xZsjx2oSYUZ6VVAoBrRCFQwUYHW9NVBgeWf6yh79t4Bnomkk66ZbbVyBCrZUYGOs2f/HUCgOuma2VYrR6CSnRToyNbngB1AoDrpmtlWK0egkh0U6Hh79t8BBKqTrplttXIEKtk5gQ7TZ62vpH0HVDqRnKARlWyrlSNQyUQFOrQXftjJe9NonZYdUBnG1AIaUcm2WjkCleyWQMfps+RE5w6oDKRvBY2oZFutHIFKpirQIXcijTh7X2/Q9sURqE66ZrbVyhGoZHcEOsieMQIdmW1VI3YrR6CSyQq0341EwxqfCW9VaYpfbdexOALVSdfMtlo5ApVMV6A95gPtea5fAoGOy7aqEbuVI1DJlAXadUb6Efp0Prmi5k/FR3qsA42oZFutHIFKpi3QLoxpfiLQ0dlWNWK3cgQq2XaBCsEN+p1CoCOzrWrEbuUIVLLVAi3bDYFqZFvViN3KEahkiwValRsC1ci2qhG7lSNQyfYItHKRs+a2gb9TzUKkF75rtlWN2K0cgUq2RaAVjzXpDYFqZFvViN3KEahkOwRaMVnD+fXi7YG/U83+5E6kjtlWNWK3cgQq2QqBNgqzBgLVyLaqEbuVI1AJAnU+vbfRn8zGVMm2qhG7lSNQyTYItNmXQ2a8c9Ckz1Lw2sURqE66ZrbVyhGoZFsF2vT2iIgmfS6jOyyOQHXSNbOtVo5AJVsg0K7+HGXQkSBQnXTNbKuVI1DJdgq05W0dEKhOuma21coRqASBegGB6qRrZlutHIFKEKgXEKhOuma21coRqASBegGB6qRrZlutHIFKpifQigobPNlyO+cSV7e6H2pxCFQnXTPbauUIVDI1gVZt2OTP9QJ1Dez0Q0MgAtVJ18y2WjkClUxLoFUfNurzeePS4k4k161FfmjcFQSqk66ZbbVyBCqZlECbfdlXoK6b2/3QvC8IVCddM9tq5QhU4lmgb353+/Dw51/W3h8lUNe8Hgg0D9fMtqoRu5UjUIlfgX734WHCD/5Y/UanT7WjP9dcA32rir/y3KEIVCddM9tq5QhU4legDw/f/TL+9pPDd/9a+cZwgTa971g8exuBbjTbqkbsVo5AJV4F+s3ttO353Yfv/FPlO10+1c7+bB0HWvOnV4M6QhGoUrpmttXKEajEq0C/OvxR/v8vKt8ZKNC29xFoARpRybZaOQKVeBXow8Nfpv+/yEW6AoEGBI2oZFutHIFKfAr0zSf5qfs3t4uLoN8r+Ho9DXJqe9/xdoNAO2R3xbWPAJCAQIeDQAGMg0CHIwRaHcg0rBe+/X3H2/TCbzTb6oms3co5hZcEboEWINCAoBGVbKuVI1DJlATquuOIO5HaQSMq2VYrR6CSCfXCI9CBoBGVbKuVI1CJ53Ggvyj9v2LgbExr3i+/zWxMGtlWNWK3cgQqmdCdSClN+mx5X74tfq7h9OnYFwSqk66ZbbVyBCrxKtA3nxz+cMS98BlN+mx5f/V26ecaTp+N+4JAddI1s61WjkAlficT+XbUbEwjMXs0mS2cylWiEajA83yg3/5u4c+fV9ufCDRsuGa2VY3YrRyBSiY1I/04zB5NZguncpVoBCpAoH6yEahKuma21coRqASB+slGoCrpmtlWK0egEgTqJxuBqqRrZlutHIFKEKifbASqkq6ZbbVyBCpBoH6yEahKuma21coRqASB+slGoCrpmtlWK0egEgTqJxuBqqRrZlutHIFKEKifbASqkq6ZbbVyBCpBoH6yEahKuma21coRqASB+slGoCrpmtlWK0egEgTqJxuBqqRrZlutHIFKEKifbASqkq6ZbbVyBCpBoH6yEahKuma21coRqASB+slGoCrpmtlWK0egEgTqJxuBqqRrZlutHIFKEKifbASqkq6ZbbVyBCpBoH6yEahKuma21coRqASB+slGoCrpmtlWK0egEgTqJxuBqqRrZlutHIFKEKifbASqkq6ZbbVyBCpBoH6yEahKuma21coRqGRTAgUAE2zGKFMBgQKARzZjlKmwIYHuOtZ+bZaYLdxu5WYLbwSBesHsL5XZwu1WbrbwRhCoF8z+Upkt3G7lZgtvBIF6wewvldnC7VZutvBGEKgXzP5SmS3cbuVmC28EgXrB7C+V2cLtVm628EYQqBfM/lKZLdxu5WYLbwSBAgAMBIECAAwEgQIADASBAgAMBIECAAwEgQIADASBAgAMBIECAAwEgY7nf/3bw8N3fv6l9m7o8M3td/+qvQ+b580fbh8e/ot/0N6NzfPt4nf90OrvehMIdDR/OEx555+0d0SDN58cGhTotx9mP/O/196RTfPNbcO/640g0LG8OHxn0RL59pPDH/xRe1cU+OrQoEAXfzV++GX85r+ZE0ny5/LL5Hfd3s/cBQIdyeJ36pfJ/999mP1vi6RFYu9gepH/sfzq8Efau7JZvrmdFv7dh9b+crhBoCP57sO85fnw8BfKu7J5Fn89/p29a6DF30x7vMj+Ytj9AOogUF9YFOjDwx8Z7ERa/s00By3QGgjUExZ/qV4sTt8NCjQp+X/+68PDH/5n7T3ZNMtroMYuXbSAQD1h7npY/jfDpkB/l/XCmzvneJMNOfl7az9yNwjUDy/M9cjmFy0MCvRFppCFTMz9zL/5P1KB/vBL7R2ZDAjUCy9uv2PusvpXaf+7TYFmTc+H1s46vrlt9S+HEwTqg68M/kZ9czut2aBA88oN1v7Q6l8ONwjUAyb/In91uMRYp3TeFy2+MMKbT6z+5XCDQEfz5uHhD00dRxl2Bbr0yAtjNxEg0DoIdDQPjR1GZSweS8UZrLmhv5zC10CgY/nKtD9NCvSb28mERAb7Ul6YHX7gBIGO5LsPl2eyJv8qWxRo/CKflMjgyAujA2CdINCRvDhEoNr7sHm+/d1Cof/mS+3d2Dz/XzIfqMXCXSBQAICBIFAAgIEgUACAgSBQAICBIFAAgIEgUACAgSBQAICBIFAAgIEgUACAgSBQAICBIFAAgIEgUACAgSBQAICBIFDoy+OPZlEUvX33Wfby9bXoiu4OAWiBQKEfjy9EOXs30zcQKNgFgUIvjiNBKk4ECnZBoNCHxJ9nP3i6+OrJe4s26G9jBAqWQaDQg9NZFF3Or30mMj33DIGCZRAo9OAois7LV0kTFIGCXRAodGfhyvSsPef07Z9+kQl0/tmF5NS+aJs+SfvpD7LX8zvRrcfXF99O1nycnPhffnYcnXmQLvkyWXLv0hcbLgTADwgUunNSaoDmLAR66VrWqXQuM+a9opNpP7HkQqA3khcLZS6+TDnzUS7QZZfU5c0WAuAHBArdOY4aztYXAo32Fo3Nl3fy7x5n10lfXs90m1hz0W59eTe9ArD4TirYVKCLJZPG56vc1Y+eAAACNklEQVR7GBS2EwQK3VkI71btzUSg6bsLU+adSueLbySvE4Fma53Ocv8eZwJdvD5fbFheGgDYFhAodMcl0OzUfdHATLR4UshwYc7kdf5funr+xWKV5Kvl62SZ+rUBgMmDQKE7LoEu25GFEDOOCoGeK3qTzsvvSGseFRIG2CYQKHTnxHENNH9TCPTVk/sfX4gKgZ6vLJgt+fqavKup7F6ArQCBQndOZ+WG4p9+87RJoI8uSCsiUNhdECh0pzIONLv6WRVoNlbp4MZvnh7VBFqcshcC5cInbDcIFHpwXBoImo1qqgr0eHm3Z0WgDddAufAJ2w0ChR6U7oV/lJ13VwS60mLe177yZrUX/mjZnsWlsJ0gUOhDcuvQXjIb0zy5KTM1p1Ogx5VroE3jQPNFT5q69wEmDwKFXtyTHT/p7UOOU/gn16Nsvjtx5n5cvxNp/+5iiU8jGqCwlSBQ6MefZssZ6X+WvlEV6Ovr+fcvfZq2K4VA50f5PfL/sXovPP6ErQSBQk/m//U9MddSwzCmdGqmvUuf593spbuM6rMxXUifr7ThGgD8gEBBAW48gt0AgcLGOCrandz6DjsCAoWNcVyblglgu0GgsDEW3kwehTx/NOPGTdgNEChsjpOiB3+fZ3jAToBAYYO8upd1utODBLsBAgUAGAgCBQAYCAIFABgIAgUAGAgCBQAYCAIFABgIAgUAGAgCBQAYCAIFABgIAgUAGAgCBQAYCAIFABgIAgUAGAgCBQAYyP8PPL/5om6TJ8EAAAAASUVORK5CYII=" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb48"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb48-1"><a href="#cb48-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot4a.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
</section>
<section id="plot-4b.-quadratic-fit" class="level2" data-number="6.3">
<h2 data-number="6.3" class="anchored" data-anchor-id="plot-4b.-quadratic-fit"><span class="header-section-number">6.3</span> Plot 4B. (Quadratic Fit)</h2>
<p>This plot again includes all 45 experiments on time to recovery.</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb50"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb50-1"><a href="#cb50-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcr, <span class="fu">aes</span>(<span class="at">x =</span> charge, <span class="at">y =</span> time_recover)) <span class="sc">+</span></span>
<span id="cb50-2"><a href="#cb50-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb50-3"><a href="#cb50-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">stat_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, </span>
<span id="cb50-4"><a href="#cb50-4" aria-hidden="true" tabindex="-1"></a>              <span class="at">formula =</span> y <span class="sc">~</span> x <span class="sc">+</span> <span class="fu">I</span>(x<span class="sc">^</span><span class="dv">2</span>), <span class="at">col =</span> <span class="st">&quot;red&quot;</span>) <span class="sc">+</span></span>
<span id="cb50-5"><a href="#cb50-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Recovery, by Charge&quot;</span>,</span>
<span id="cb50-6"><a href="#cb50-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">glue</span>(<span class="fu">nrow</span>(tcr), <span class="st">&quot; experiments on time to complete recovery&quot;</span>),</span>
<span id="cb50-7"><a href="#cb50-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Charge&quot;</span>,</span>
<span id="cb50-8"><a href="#cb50-8" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Recovery (seconds)&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img 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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb51"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb51-1"><a href="#cb51-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot4b.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
<p>The quadratic model shown in plot 4B is summarized below:</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb53"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb53-1"><a href="#cb53-1" aria-hidden="true" tabindex="-1"></a>m4b <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_recover <span class="sc">~</span> charge <span class="sc">+</span> <span class="fu">I</span>(charge<span class="sc">^</span><span class="dv">2</span>), <span class="at">data =</span> tcr)</span>
<span id="cb53-2"><a href="#cb53-2" aria-hidden="true" tabindex="-1"></a>m4bt <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m4b, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb53-3"><a href="#cb53-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb53-4"><a href="#cb53-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb53-5"><a href="#cb53-5" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m4bt, <span class="at">digits =</span> <span class="dv">3</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: left;">term</th>
<th style="text-align: right;">estimate</th>
<th style="text-align: right;">std.error</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">conf.low</th>
<th style="text-align: right;">conf.high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">(Intercept)</td>
<td style="text-align: right;">24.840</td>
<td style="text-align: right;">44.088</td>
<td style="text-align: right;">0.576</td>
<td style="text-align: right;">-64.132</td>
<td style="text-align: right;">113.813</td>
</tr>
<tr class="even">
<td style="text-align: left;">charge</td>
<td style="text-align: right;">18.275</td>
<td style="text-align: right;">28.105</td>
<td style="text-align: right;">0.519</td>
<td style="text-align: right;">-38.445</td>
<td style="text-align: right;">74.994</td>
</tr>
<tr class="odd">
<td style="text-align: left;">I(charge^2)</td>
<td style="text-align: right;">3.064</td>
<td style="text-align: right;">3.535</td>
<td style="text-align: right;">0.391</td>
<td style="text-align: right;">-4.069</td>
<td style="text-align: right;">10.198</td>
</tr>
</tbody>
</table>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb54"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb54-1"><a href="#cb54-1" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m4b) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb54-2"><a href="#cb54-2" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb54-3"><a href="#cb54-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: right;">r.squared</th>
<th style="text-align: right;">adj.r.squared</th>
<th style="text-align: right;">sigma</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">df</th>
<th style="text-align: right;">df.residual</th>
<th style="text-align: right;">nobs</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: right;">0.4337204</td>
<td style="text-align: right;">0.4067547</td>
<td style="text-align: right;">94.08093</td>
<td style="text-align: right;">6.5e-06</td>
<td style="text-align: right;">2</td>
<td style="text-align: right;">42</td>
<td style="text-align: right;">45</td>
</tr>
</tbody>
</table>
</div>
</div>
</section>
<section id="plot-4c.-comparing-the-linear-to-the-quadratic-fit" class="level2" data-number="6.4">
<h2 data-number="6.4" class="anchored" data-anchor-id="plot-4c.-comparing-the-linear-to-the-quadratic-fit"><span class="header-section-number">6.4</span> Plot 4C. Comparing the Linear to the Quadratic Fit</h2>
<p>As we saw above, the <em>p</em> value for adding the quadratic term for Model 4 is about 0.391, so we won’t bother, but here’s the plot comparing the two fits.</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb55"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb55-1"><a href="#cb55-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcr, <span class="fu">aes</span>(<span class="at">x =</span> charge, <span class="at">y =</span> time_recover)) <span class="sc">+</span></span>
<span id="cb55-2"><a href="#cb55-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb55-3"><a href="#cb55-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">stat_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, </span>
<span id="cb55-4"><a href="#cb55-4" aria-hidden="true" tabindex="-1"></a>              <span class="at">formula =</span> y <span class="sc">~</span> x <span class="sc">+</span> <span class="fu">I</span>(x<span class="sc">^</span><span class="dv">2</span>), <span class="at">col =</span> <span class="st">&quot;red&quot;</span>) <span class="sc">+</span></span>
<span id="cb55-5"><a href="#cb55-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, <span class="at">formula =</span> y <span class="sc">~</span> x, <span class="at">col =</span> <span class="st">&quot;blue&quot;</span>) <span class="sc">+</span></span>
<span id="cb55-6"><a href="#cb55-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Recovery, by Charge&quot;</span>,</span>
<span id="cb55-7"><a href="#cb55-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">glue</span>(<span class="fu">nrow</span>(tcr), <span class="st">&quot; experiments on time to complete recovery&quot;</span>),</span>
<span id="cb55-8"><a href="#cb55-8" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Charge&quot;</span>,</span>
<span id="cb55-9"><a href="#cb55-9" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Recovery (seconds)&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img 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+f51zfmUdBafEGzF763nNbyzvbqOmb6ivVd83wsuL2WkUd+GbXN++TV/HY/0SlseUR5XOZ5sBWPqj8v4bMVIdKysQCtOo0WAVruwlk7QWeJVSXVfOean7sfz/fzYqH5u/Ndy/1wlbPzDywS61ruLFkIcJcs3i+XXJxULhNlecBc34dfLY6NZh8vft9afEE7QHvJcZd3t+fqWMlofCy4PdfIajstGas/I155K789fnYJO1q+O/s/3Iony78DOoMXTDYWoEeLI7t5AMz2mvN3HxdXv7JGJKwONqrdptjl/lP1mfnhaJ4vjmHcD9fiZZlYywBcnivPD3qaSy738IXAZYB6u7pWv56fhLYWX31rJ0D7yXGXd7e34GQVNfPVND8W3J5rZLWdlgz3z4gjb7V+9xNrhFU9iPNT83ArVuJ0Bi+wbCpAT5ZJ4p6NHdXPSus76OrwJ6s+UutMKN5sfbh2/LdMLDfYyn23teRqzceNAD0p+jY+b32b1beYL9ha3PkOsXLc5d3tLWhtrfmx0PZcI1f2OTJqfV8+ecv1tz6xRlh1OnJcXZ7paMX55VWdwQsoGwrQ5/vVIUg7QBuRUD+zXxyaFl0MFxfvLg6/ivW0PlzbF91dv5kgrSVXa24GaD7vejn33qO65NouPv+xtfiCVoD2k+Ms39qeu/qArND2XCNXf0UcGUerK6Y+ecv1tz6xRtjs/Yu1MA22YnkgqzN4wWVDAXpc30ma+0Nj//Dtq8Uh6K3Fu7eWK9xMgM4H/2T1kVOeA67pA9R/CNjKqdaxXDBA60ZuIEAd/SfFh5Zagq1YRqzO4AUXXoC6RxtlV/J8d2wfgV50P9o7QJtLhgN0xtcfXSpUNy40WB+BBo6xNn0E6shYBqhfXj1Am59YdwRa3FMRWrsbtyc6gxdYUgXo6lS+0S3i9mLPF7256kxe7fflNdDmh3sHaGvJzgDN89V4I2dtq9PQMQHq+dbN5UPXQOsj0i+2P9YvQOvXQB0Zrfxuslx/6xNrhJV9ipWIrlYs5R7pDF5g2fR8oItd6Xgxemm2u9Q7upfdxYsdqxiJ+T/uLMd/V/vafOdzP9w7QFtLBgK0scJaMh4vj0dPsqoXfkyAtr+1s7y7vQX1Eem32h/rCtCGkcsYdWQs89svr77+5ifWCCs7FR9Uy3S0Yjninz1pgthtUgVoceXr6qP50Op6YhR5OX9Z7YPl7lX9tljoWrWaahxo48P9A9RdMnQEuhx/00wOzzjQUQHa+tbO8uvGgebL4Z2Nj3UFaMPIfDkO1DV/MRDCK2+1fo+fXcKK37zxr6qFO1qxWP7cvs7gBZZUAVo7qW+OtDye99cszplPsmpAYnWgl2V/+bgcPHrN8+H+Aeou2UrAIjlPHxW/33u/GK56pxlstTtriuWCAVqtJlZOa3lne0tOgjf8VGfH4QBtGLk60m3IqG/eI6/mt/uJbmHzFl04Gm7F+XMLdAYvsCQL0NXd0c5I9WWyVsG2GEI/72y53Lz/s/HhmAB1lmwl4El1rfb5/uJzrTtO5+oX98L7A3Sxmkg5reUH3wsfDNCmka174ecvW5t3rgzXos79RKew+aqXTgVbsXaPlxBE0gXofAqe5vRF1e+LTu/5PD5Htbv65rMxFQdNWX3On9WHowK0uWQ7AR/MxF0sVvHpwexj5642BoIu1O9Vvw4G6GI1kXJayzvbc3W4kx5VH+vsRGoYWZuNqSGjtnmPvEbUuZ/oEtawzF22Oe6zPXBYCA5b9VTOxk4nhrNFRh7rDF6AUYDuIttj5OkdncELMArQXWR7jHyuPnhBRgG6i2yNkS/ubIlQsaMoQHeRLTGy6J3XAaggowDdRbbEyJMsO996CJQQILYqQIUQgoQCVAghBqIAFUKIgShAhRBiIApQIYQYiAJUCCEGogAVQoiBKECFEGIgClAhhBiIAlQIIQaiABVCiIEoQIUQYiAKUCGEGMiGAvSfCyHEGSBNgG5mMx6eJNtyN0+wwlILCCDDIsEaRnWsl2EKUAjY8sbqwgpLLSAA1jCqYwpQH9DG4pY3VhdWWGoBAbCGUR1TgPqANha3vLG6sMJSCwiANYzqmALUB7SxuOWN1YUVllpAAKxhVMcUoD6gjcUtb6wurLDUAgJgDaM6pgD1AW0sbnljdWGFpRYQAGsY1TEFqA9oY3HLG6sLKyy1gABYw6iOKUB9QBuLW95YXVhhqQUEwBpGdUwB6gPaWNzyxurCCkstIADWMKpjClAf0MbiljdWF1ZYagEBsIZRHVOA+oA2Fre8sbqwwlILCIA1jOqYAtQHtLG45Y3VhRWWWkAArGFUxxSgPqCNxS1vrC6ssNQCAmANozqmAPUBbSxueWN1YYWlFhAAaxjVMQWoD2hjccsbqwsrLLWAAFjDqI4pQH1AG4tb3lhdWGGpBQTAGkZ1TAHqA9pY3PLG6sIKSy0gANYwqmObC9DXv7t9ePgv/6Z68ZvZi1985XlR3+yQzZgAbSxueWN1YYWlFhAAaxjVsY0F6HcfHJb8dfHi+/mLH/6+9aKx2QGbsQHaWNzyxurCCkstIADWMKpjmwrQ1x8f/uir/PV/O3zrH2ev7h++/VX+3ceHb//ZfdHYbPxmjIA2Fre8sbqwwlILCIA1jOrYpgL0WXWE+c3hj/P829vli+8/KNK08aK52fjNGAFtLG55Y3VhhaUWEABrGNWxDQXo7AD0b1evyhQt//+l86K52ejNWAFtLG55Y3VhhaUWEABrGNWxDQXo9x/UL3Her9L0WZGdjRfNzUZvxgpoY3HLG6sLKyy1gABYw6iObShAv7399p//5785PPzRf86Lw9HqbL34bePFYnMVT4QQYtsxCdDfzHvhf6kAFUJsIVnBkAXHB+izYgDTn/PXvyt64WuZ+cPfN140F9IpvAv2BAurCysstYAAWMPSO5YtaPx2Q6fwzw6rLqL7hz9eewS63Gz0ZqxI3lgBsOWN1YUVllpAAKxhqR3L6tR+v7FroKHMVIBGgC1vrC6ssNQCAmANS+xYlgUSdGMBWp2flz+oF34g2PLG6sIKSy0gANawHQ/Q5XHms+KGo8WQz2ocaO1Fc7PRm7GCWkXY8sbqwgpLLSAA1rC0jmUuy3c2dSfS/er48n4Rk7oTaSDY8sbqwgpLLSAA1rBdD9BvbxfzLc174asb46vb3xsvmpuN34wR1CrCljdWF1ZYagEBsIYldayVn6sE3dhsTM9ul8NA3yoveH5Xn4DpO83G1BdseWN1YYWlFhAAa9jOB2j+XTHr57/9avFiFpm/+LPnRX2zQzZjArWKsOWN1YUVllpAAKxhCtB4FKAu2PLG6sIKSy0gANYwBWg8ClAXbHljdWGFpRYQAGuYAjQeBagLtryxurDCUgsIgDVs13vhh6AAdcGWN1YXVlhqAQGwhilA41GAumDLG6sLKyy1gABYw3b8TqRBKEBdsOWN1YUVllpAAKxhCtB4FKAu2PLG6sIKSy0gANaw1I4F8lMB6oVaRdjyxurCCkstIADWsPSO+eJTAeoneWMFwJY3VhdWWGoBAbCGERxrx6cC1A+gsbxgyxurCysstYAAWMOojilAfUAbi1veWF1YYakFBMAaRnVMAeoD2ljc8sbqwgpLLSAA1jCqYwpQH9DG4pY3VhdWWGoBAbCGUR1TgPqANha3vLG6sMJSCwiANYzqmALUB7SxuOWN1YUVllpAAKxhVMcUoD6gjcUtb6wurLDUAgJgDaM6pgD1AW0sbnljdWGFpRYQAGsY1TEFqA9oY3HLG6sLKyy1gABYw6iOKUB9QBuLW95YXVhhqQUEwBpGdUwB6gPaWNzyxurCCkstIADWMKpjClAf0MbiljdWF1ZYagEBsIZRHVOA+oA2Fre8sbqwwlILCIA1jOqYAtQHtLG45Y3VhRWWWkAArGFUxxSgPqCNxS1vrC6ssNQCAmANozqmAPUBbSxueWN1YYWlFhAAaxjVMQWoD2hjccsbqwsrLLWAAFjDqI4pQH1AG4tb3lhdWGGpBQTAGkZ1TAHqA9pY3PLG6sIKSy0gANYwqmMKUB/QxuKWN1YXVlhqAQGwhlEdU4D6gDYWt7yxurDCUgsIgDWM6pgC1Ae0sbjljdWFFZZaQACsYVTHFKA+oI3FLW+sLqyw1AICYA2jOqYA9QFtLG55Y3VhhaUWEABrGNUxBagPaGNxyxurCysstYAAWMOojilAfUAbi1veWF1YYakFBMAaRnVMAeoD2ljc8sbqwgpLLSAA1jCqYwpQH9DG4pY3VhdWWGoBAbCGUR1TgPqANha3vLG6sMJSCwiANYzqmALUB7SxuOWN1YUVllpAAKxhVMcUoD6gjcUtb6wurLDUAgJgDaM6pgD1AW0sbnljdWGFpRYQAGsY1TEFqA9oY3HLG6sLKyy1gABYw6iOKUB9QBuLW95YXVhhqQUEwBpGdUwB6gPaWNzyxurCCkstIADWMKpjClAf0MbiljdWF1ZYagEBsIZRHVOA+oA2Fre8sbqwwlILCIA1jOqYAtQHtLG45Y3VhRWWWkAArGFUxxSgPqCNxS1vrC6ssNQCAmANozqmAPUBbSxueWN1YYWlFhAAaxjVMQWoD2hjccsbqwsrLLWAAFjDqI4pQH1AG4tb3lhdWGGpBQTAGkZ1TAHqA9pY3PLG6sIKSy0gANYwqmMKUB/QxuKWN1YXVlhqAQGwhlEdU4D6gDYWt7yxurDCUgsIgDWM6pgC1Ae0sbjljdWFFZZaQACsYVTHFKA+oI3FLW+sLqyw1AICYA2jOqYA9QFtLG55Y3VhhaUWEABrGNUxBagPaGNxyxurCysstYAAWMOojilAfUAbi1veWF1YYakFBMAaRnVMAeoD2ljc8sbqwgpLLSAA1jCqYwpQH9DG4pY3VhdWWGoBAbCGUR1TgPqANha3vLG6sMJSCwiANYzqmALUB7SxuOWN1YUVllpAAKxhVMcUoD6gjcUtb6wurLDUAgJgDaM6pgD1AW0sbnljdWGFpRYQAGsY1TEFqA9oY3HLG6sLKyy1gABYw6iOKUB9QBuLW95YXVhhqQUEwBpGdUwB6gPaWNzyxurCCkstIADWMKpjClAf0MbiljdWF1ZYagEBsIZRHVOA+oA2Fre8sbqwwlILCIA1jOqYAtQHtLG45Y3VhRWWWkAArGFUxxSgPqCNxS1vrC6ssNQCAmANozqmAPUBbSxueWN1YYWlFhAAaxjVMQWoD2hjccsbqwsrLLWAAFjDqI4pQH1AG4tb3lhdWGGpBQTAGkZ1TAHqA9pY3PLG6sIKSy0gANYwqmMKUB/QxuKWN1YXVlhqAQGwhlEdU4D6gDYWt7yxurDCUgsIgDWM6pgC1Ae0sbjljdWFFZZaQACsYVTHFKA+oI3FLW+sLqyw1AICYA2jOqYA9QFtLG55Y3VhhaUWEABrGNUxBagPaGNxyxurCysstYAAWMOojilAfUAbi1veWF1YYakFBMAaRnVMAeoD2ljc8sbqwgpLLSAA1jCqYwpQH9DG4pY3VhdWWGoBAbCGUR1TgPqANha3vLG6sMJSCwiANYzqmALUB7SxuOWN1YUVllpAAKxhVMcUoD6gjcUtb6wurLDUAgJgDaM6pgD1AW0sbnljdWGFpRYQAGsY1TEFqA9oY3HLG6sLKyy1gABYw6iOKUB9QBuLW95YXVhhqQUEwBpGdUwB6gPaWNzyxurCCkstIADWMKpjClAf0MbiljdWF1ZYagEBsIZRHVOA+oA2Fre8sbqwwlILCIA1jOqYAtQHtLG45Y3VhRWWWkAArGFUxxSgPqCNxS1vrC6ssNQCAmANozqmAPUBbSxueWN1YYWlFhAAaxjVMQWoD2hjccsbqwsrLLWAAFjDqI4pQH1AG4tb3lhdWGGpBQTAGkZ1TAHqA9pY3PLG6sIKSy0gANYwqmMKUB/QxuKWN1YXVlhqAQGwhlEdU4D6gDYWt7yxurDCUgsIgDWM6phxgL787Fc3b9782WePxqmqNmuxkkFAG4tb3lhdWGGpBQTAGkZ1zDJAv34nW3Hl89HaFKAu2PLG6sIKSy0gANYwqmN2AfpgP2ty7v2R2hSgLtjyxurCCkstIADWMKpjVgH68MYsMs+/9/n83P3lH391qXh9d5Q2BagLtryxurDCUgsIgDWM6phNgJ7eK9LycfN3D2YZevWx+9EIFKAu2PLG6sIKSy0gANYwqmMmAfrqTnbed8lzdlh64cuBwnIFaBtseWN1YYWlFhAAaxjVMZsA/UnocufD/21MgD4RQohtR+NAIWCPD7C6sMJSCwiANYzqmAbS+4A2Fre8sbqwwlILCIA1jOqYAtQHtLG45Y3VhRWWWkAArGFUx6YI0NM/fvbbwYLqm7VYySCgjcUtb6wurLDUAgJgDaM6ZhugL/7j4zx/VQ4J/fVIYbkCtA22vLG6sMJSCwiANYzqmGmAHmdvfJnnR+V9SG+M6H5fbHb0GoYCbSxueWN1YYWlFhAAaxjVMcsAPSlj8/l+duHxi+vZtdHaFKAu2PLG6sIKSy0gANYwqmOWAXo0S84iRvd+Xfx7YcxNSPPNjl3BYKCNxS1vrA1LJJ4AACAASURBVC6ssNQCAmANozpmGKCnd4rkrGL01fXx5/AKUBdseWN1YYWlFhAAaxjVMcMAnWfmq+vZxVwBOg3Y8sbqwgpLLSAA1jCqY+YB+nw/u5UrQKcBW95YXVhhqQUEwBpGdcz8FP64vASqa6CTgC1vrC6ssNQCAmANozpm24l0seh+L5JTvfCTgC1vrC6ssNQCAmANozpmPIyp4FZ++mE2Pw4dhwLUBVveWF1YYakFBMAaRnXMeCD9jItlR9LerbHKFKBtsOWN1YUVllpAAKxhVMeMb+X86KfFUzxe/eTKF+NkzTdrsI5hQBuLW95YXVhhqQUEwBpGdUyzMfmANha3vLG6sMJSCwiANYzqmALUB7SxuOWN1YUVllpAAKxhVMdsAvTln9o8Gq1NAeqCLW+sLqyw1AICYA2jOmbzTKTrWRsNpLcHW95YXVhhqQUEwBpGdUwB6gPaWNzyxurCCkstIADWMKpjJgF6+sfPSj7Jsr2f/sNnn/3qUrb33m91J5I52PLG6sIKSy0gANYwqmOWnUivVrcfPdCEylOALW+sLqyw1AICYA2jOmZ7K+fq9s3jck6mcShAXbDljdWFFZZaQACsYVTH7OcDnfN8X5OJ2IMtb6wurLDUAgJgDaM6Zj6dne/FQBSgLtjyxurCCkstIADWMKpjpgHaOAJVgNqDLW+sLqyw1AICYA2jOmY8nZ3356EoQF2w5Y3VhRWWWkAArGFUx4yns7s6v/B5ei/Lxk/HpAB1wZY3VhdWWGoBAbCGUR0zvRe+eCL8mzdv3rw0+//qWGUK0DbY8sbqwgpLLSAA1jCqY6YBWhx4Voyfj14B2gZb3lhdWGGpBQTAGkZ1zHg2ppefFkef594bP5NIrgBtgy1vrC6ssNQCAmANozqm6ex8QBuLW95YXVhhqQUEwBpGdUwB6gPaWNzyxurCCkstIADWMKpj5gGq+UAnBFveWF1YYakFBMAaRnXMNEBffKjp7CYFW95YXVhhqQUEwBpGdcx4NiYF6KRgyxurCysstYAAWMOojlkG6HGWnf/ZZws0H6g92PLG6sIKSy0gANYwqmOmszEZ3L7Z2Kzp2mKANha3vLG6sMJSCwiANYzq2FSTiVigAHXBljdWF1ZYagEBsIZRHZtqOjsLFKAu2PLG6sIKSy0gANYwqmNTTahsgQLUBVveWF1YYakFBMAaRnXMthPJ4g742mZN1xYDtLG45Y3VhRWWWkAArGFUxywDdHYI+v5oQfXNWq4sCmhjccsbqwsrLLWAAFjDqI5ZnsJ/9G6W7V2+WfFTDWMyB1veWF1YYakFBMAaRnXMtBMp00D6acGWN1YXVlhqAQGwhlEdU4D6gDYWt7yxurDCUgsIgDWM6phmY/IBbSxueWN1YYWlFhAAaxjVMQWoD2hjccsbqwsrLLWAAFjDqI4pQH1AG4tb3lhdWGGpBQTAGkZ1zPyRHgdZtnegR3pMA7a8sbqwwlILCIA1jOqYbYAe66Fyk4Itb6wurLDUAgJgDaM6ZhqgRX6eu3zz3Us2CaoAdcGWN1YXVlhqAQGwhlEdswzQ5/vZhS/Kn17cyQzui1eAumDLG6sLKyy1gABYw6iOWQboUXZhcfeRydygClAXbHljdWGFpRYQAGsY1bGpZmN6vn9Bt3Kagy1vrC6ssNQCAmANozo21XygFpODKkBdsOWN1YUVllpAAKxhVMcUoD6gjcUtb6wurLDUAgJgDaM6ZvtMpFvLFyeZTuHtwZY3VhdWWGoBAbCGUR1TJ5IPaGNxyxurCysstYAAWMOojhkPYzr/efnT1zc0jGkKsOWN1YUVllpAAKxhVMfMB9JnBwcHRrciKUBdsOWN1YUVllpAAKxhVMdsb+V8sF/dyWnybA8FqAu2vLG6sMJSCwiANYzqmPFkIqcP350dgV6+O7oDqdysxUoGAW0sbnljdWGFpRYQAGsY1TFNZ+cD2ljc8sbqwgpLLSAA1jCqYwpQH9DG4pY3VhdWWGoBAbCGUR2bJED/NFSNs1mb1QwA2ljc8sbqwgpLLSAA1jCqY7YBevrpm1+WT5e78sVIXeVmDdYxDGhjccsbqwsrLLWAAFjDqI6ZBujJfvkszuLxnHu3Ap+JQAHqgi1vrC6ssNQCAmANozpmPJB+fi/SHz/c10D6KcCWN1YXVlhqAQGwhlEds72V8/zizF23ck4CtryxurDCUgsIgDWM6pjpbEyN+UA1G5M92PLG6sIKSy0gANYwqmOazs4HtLG45Y3VhRWWWkAArGFUx3QE6gPaWNzyxurCCkstIADWMKpjttdAL3p/HooC1AVb3lhdWGGpBQTAGkZ1zDJAT7LsyqPyp5f3smz8OCYFqAu2vLG6sMJSCwiANYzqmOk40KNiHqaDg4NiTqbxB6AK0BbY8sbqwgpLLSAA1jCqY6YBevpJtpjO7udjheUK0DbY8sbqwgpLLSAA1jCqY9NMZ/f3ms5uErDljdWFFZZaQACsYVTHNBuTD2hjccsbqwsrLLWAAFjDqI7ZB+jpoxFympu1WlE00MbiljdWF1ZYagEBsIZRHTMO0IfvZNkbX776yXsW5/AKUBdseWN1YYWlFhAAaxjVMdtOpHtFB9IsQK9n50cPo1eAtsGWN1YXVlhqAQGwhlEdsx7GdP6v9t/48vTvstUj4oejAHXBljdWF1ZYagEBsIZRHTMeSP9+dRP8g30NpJ8AbHljdWGFpRYQAGsY1THbWzmvLWcROdatnBOALW+sLqyw1AICYA2jOmYYoKd3islEqgDVZCJTgC1vrC6ssNQCAmANozpmPp1dFaCazm4KsOWN1YUVllpAAKxhVMcUoD6gjcUtb6wurLDUAgJgDaM6ZnoKX3QcVcl5YtANrwB1wZY3VhdWWGoBAbCGUR2z7EQqO47mAapnIk0CtryxurDCUgsIgDWM6pjxUzmvPi4D9MUNPZVzCrDljdWFFZZaQACsYVTHTAfSH2dZdrC/d/nS7P9rY5UpQNtgyxurCysstYAAWMOojtneC/+H/cWEoAb5qQBtgS1vrC6ssNQCAmANozpmPJnIy08PZul57soXwU9EoAB1wZY3VhdWWGoBAbCGUR3TfKA+oI3FLW+sLqyw6v/ydC2pkiZYw6glpgD1AW0sbnljdWGFlf8uLnhxIhRrGLXEpgjQ0z9+9tvBguqbtVjJIKCNxS1vrC6ssLwWn6AIxRpGLTHbAH3xHx/n+asbs3o4P34UkwK0Bba8sbqwwpz8pCQo1jBqiRkPYyoG0R9l82mVx0pTgLbAljdWF1aYAjQSqDDj+UBnsfl8P7vw+MV1g4FMClAXbHljdWGFufkJSVCsYdQSs50PtLj//aS8CUn3wk8BtryxurDCFKCRQIWZzwdaxahmY5oCbHljdWGFtfKTkaBYw6glNsF0duU0IgrQKcCWN1YXVpgCNBKoMPMAfT5/GpICdAqw5Y3VhRWmAI0EKsz8FP54Pg+TroFOAba8sbqwwhSgkUCF2XYiXSy634vkVC+8MfM9DFveWF1YYQrQSKDCjIcxFdzKTz/MNB+oJct9jCZsAVYXVph64SOBCjOfD3Q+K322N/6x8ArQCt5u1oJl2ApsHihAY4EKM76V86Of3p399+onJvPZKUBLiPuZC8qwGtg80J1IsUCFaTYmH6jGQu5oDijDamDzQAEaC1TYZgP029tv/7n84fVvbh8e/uKrvP2ivtmhmxkNqbGYp3oOJMPqYPNAszHFAhVmEqCn4dnr/lgfzPT648N5gH7/wWHBD3/fetHYbA9p00BqLAXoCLB5oPlAY4EKMwnQV9fP+695vrjRGE7/zWEVoPcP3/4q/66K08aLxmZ7SJsGUGO18hO0t60AGdYAmwcLXbQGxRpGLTGbU/jjLPN0G319o/lwuW9vVwH67e3ycPP7D976R+dFc7M9pE0DqLEUoGPA5gFWF1UY1TGja6AvZll57r1H9d/8at+ZVnl2Av/v59dAvzn8cfmbbw5/6bxobraHtGkANZYCdAzYPMDqogqjOmbWifSgfKLxmzf/4bPPPvunm8WzObO99xt3c94//HHViXT/8G/L3zwrsrPxornZfl9hAkCNpQAdAzYPsLqowqiO2fXCnz7Yb+zm55rxOQvIt/88D9DXH1dn68XLxovF5iqeCN8tf6klCSFi6DuM6cWvDqp9/KBxOl9QXuNUgMaj/BRiu4kaB/qnP/3J9+v7xSXOVoD+8PeNF81FdApfsA1n8CjD6mDPSLG6qMKojm1qIP03Zf97vyPQ5WbjN2MEqrG2ID9ZhtXA5gFWF1UY1bENBei3t8uYVIAOQAE6HGweYHVRhVEd21CAfnO4ZHairl74KPj5CTNsBTYPsLqowqiOJQnQxZDPahxo7UVzs9GbsYLWWKsOpNRKAmB1YYWlFhAAaxjVsSSTiehOpGjmR5/Y8sbqwgpLLSAA1jCqY0kC9PXHhz9a3v7eeNHc7NDNjAbaWNzyxurCCkstIADWMKpjaaaz+64+AdN3mo2pL9jyxurCCkstIADWMKpjieYD/e43s8j8RXXI2XhR3+zQzYwG2ljc8sbqwgpLLSAA1jCqY5qR3ge0sbjljdWFFZZaQACsYVTHFKA+oI3FLW+sLqyw1AICYA2jOmYYoK9+ctW9AX4cClAXbHljdWGFpRYQAGsY1THLAL1ezAn62PveIBSgLtjyxurCCkstIADWMKpjhgF6eq8crHj+rlWGKkBdsOWN1YUVllpAAKxhVMdsr4F+/U6ZoW/eHamq2qzJWoYAbSxueWN1YYWlFhAAaxjVMfNOpIfzDPU8IikaBagLtryxurDCUgsIgDWM6tgEvfCnD28UEbrXmlM5FgWoC7a8sbqwwlILCIA1jOrYNMOYXn44f0LSuMNQBagLtryxurDCUgsIgDWM6tgUR6CfXlrOHnR1uDIFaBtseWN1YYWlFhAAaxjVMesAXaRn0Rf/8l7zufCxKEBdsOWN1YUVllpAAKxhVMdMA7S6+rkaDXqSXRgxpkkB6oItb6wurLDUAgJgDaM6ZjkO9E7mdh69uv7Gl8O1KUBdsOWN1YUVllpAAKxhVMeM70Taaw5fenVdR6CWYMsbqwsrLLWAAFjDqI6ZBuiVz51fnf52zFAmBagLtryxurDCUgsIgDWM6phmY/IBbSxueWN1YYWlFhAAaxjVMcsAPbK4/ai+WdO1xQBtLG55Y3VhhaUWEABrGNUx22ugt8YLqm/WdG0xQBuLW95YXVhhqQUEwBpGdcw0QMd0ufs2a7q2GKCNxS1vrC6ssNQCAmANozpmOoxp79fjBdU3a7q2GKCNxS1vrC6ssNQCAmANozpmeQ30OLtgehFUAeqCLW+sLqyw1AICYA2jOmYZoC8/ybJzl29W/HT0vMoKUBdseWN1YYWlFhAAaxjVMeOB9DXGXxBVgLpgyxurCysstYAAWMOojilAfUAbi1veWF1YYakFBMAaRnVMA+l9QBuLW95YXVhhqQUEwBpGdUwB6gPaWNzyxurCCkstIADWMKpj9gF6avZweAWoC7a8sbqwwlILCIA1jOqYcYAWj5R748tXPzF5PLwC1AVb3lhdWGGpBQTAGkZ1zHZC5Xvz3qNX17PzBjclKUBdsOWN1YUVllpAAKxhVMdMA/Qoy87/1f4bX57+XTZqKvrFZkevYSjQxuKWN1YXVlhqAQGwhlEdswzQkyx7v7oj/sG+wcQiClAXbHljdWGFpRYQAGsY1THT6eyKR8hVU4ocZxfHSlOAtsCWN1YXVlhqAQGwhlEdM59MpArQ5/saSG8PtryxurDCUgsIgDWM6pj5dHZVgFrMbacAdcGWN1YXVlhqAQGwhlEdU4D6gDYWt7yxurDCUgsIgDWM6pjtY41vLZNz3BPhq82OXcFgoI3FLW+sLqyw1AICYA2jOmY7H+jFRYDOwlSdSPZgyxurCysstYAAWMOojlkG6PP97OrjMkBf3MgMZqdXgLpgyxurCysstYAAWMMIjj192v6d6UD64yzLDvb3Ll+a/X8tVp1ns+NXMRBAY3nBljdWF1ZYagEBsIald+zp08kDNP/D/mI2UIP8VIC2wJY3VhdWWGoBAbCGpXbs6dNNBGj+8tODWXqes3lAvALUBVveWF1YYakFBMAaltaxpwta72g+UB/UKsKWN1YXVlhqAQGwhqV07OlTBWgc1CrCljdWF1ZYagEBsIalc+xpV37aDqS3OXNfbdZ0bTFQqwhb3lhdWGGpBQTAGpbKse74NA7QLNszmUl5sVm7VUVCrSJseWN1YYWlFhAAa1gax9bFp+2dSPfKPvg378Zp7Nis1YqioVYRtryxurDCUgsIgDUshWPr49P6GmjxRI/ZYajRqbwC1AVb3lhdWGGpBQTAGrZ5x/rEp30n0umDYhR9du59g1N5BagLtryxurDCUgsIgDVs0471i89JeuFfflqeyv+FJhMxB1veWF1YYakFBMAatlnH+sbnVMOYXnxYPFuux5q7Nzt2BYOhVhG2vLG6sMJSCwiANWyTjvWPz2kC9PTTS5kCdAqw5Y3VhRWWWkAArGGbcywmPicI0NOHN6wugipAXbDljdWFFZZaQACsYZtyLC4+zQPUtBteAeqCLW+sLqyw1AICYA3bjGOx8WkboMWVzxnnrQaCKkBdsOWN1YUVllpAAKxhm3AsPj7N70QyvRVJAeqCLW+sLqyw1AICYA2b3rEh8WkdoFc+77/lHpu1XFkU1CrCljdWF1ZYagEBsIZN7diw+LS9lfO3hvfBl5u1XV0E1CrCljdWF1ZYagEBsIZN69jQ+NR0dn6oVYQtb6wurLDUAgJgDZvSseHxaR6g5Yz0ewfvPYrV4d2sxUoGQa0ibHljdWGFpRYQAGvYdI6NiU/rAD3O9EykKcGWN1YXVlhqAQGwhk3l2Lj4NA7QIj/PXb75rp7KORHY8sbqwgpLLSAA1rBpHBsbn7YB+nw/uzAfQf/ijp4LPwXY8sbqwgpLLSAA1rApHBsfn7YBepRdWPTDn97JLg6VtNrs6DUMhVpF2PLG6sIKSy0gANYwe8cs4tN2GNOd2lHn8/0Lowc1KUBdsOWN1YUVllpAAKxh1o7ZxKfxQPraBEyNFwNRgLpgyxurCysstYAAWMNsHbOKTwWoH2oVYcsbqwsrLLWAAFjDLB2zi0/jU/js1vLFSaZTeHuw5Y3VhRWWWkAArGF2jlnGpzqR/FCrCFveWF1YYakFBMAaZuWYbXyaD2M6P59N5OsbGsY0BdjyxurCCkstIADWMBvHrONzgoH02cHBgdGtSApQF2x5Y3VhhaUWEABrmIVj9vFpfSvng/3qTs6990fqKjdrsI5hUKsIW95YXVhhqQUEwBo23rEp4tN8MpHTh+/OjkAv3zWZ2E4B6oItb6wurLDUAgJgDRvr2DTxqens/FCrCFveWF1YYakFBMAaNsqxp1PFpwLUD7WKsOWN1YUVllpAAKxhIxybMD4nCtA/DVXjbNZmNQOgVhG2vLG6sMJSCwiANWywY5PGp3WAnn765pfzhyNZPNdYAeqCLW+sLqyw1AICYA0b6NjE8WkcoCf72RvzAM32bgU+E4EC1AVb3lhdWGGpBQTAGjbIscnj03wg/fxepD9+uK+B9FOALW+sLqyw1AICYA0b4Nj06Zlb38p5fnHmrls5JwFb3lhdWGGpBQTAGhbt2Ebi03g2psZ8oJqNyR5seWN1YYWlFhAAa1ikYxuKT01n54daRdjyxurCCkstIADWsCjHJorPH/yg/TsdgfqgVhG2vLG6sMJSCwiANSzCsWni8wcFHlmm10Aven8eigLUBVveWF1YYakFBMAa1tuxKeLzBws8sgwD9CTLrjwqf3p5L8vGj2NSgLpgyxurCysstYAAWMN6OjZBfP6gTluW5TjQo2IepoODg2JOpvEHoArQFtjyxurCCkstIADWsF6O2cfnDxzasiwD9PSTbDGd3c+jpXo2a7COYVCrCFveWF1YYakFBMAa1sMx8/h009NzBj/RdHZ/r+nsJgFb3lhdWGGpBQTAGrbWMev47JOeuWZj8kOtImx5Y3VhhaUWEABr2BrHjOOzX3iWsswD9PRRjzX2QgHqgi1vrC6ssNQCAmAN63TMNj77p2duHqAP38myN7589ZP3LM7hFaAu2PLG6sIKSy0gANawDsdM4zMqPXPrTqR7RQfSLECvZ+dHD6NXgLbBljdWF1ZYagEBsIYFHbOMz9j0zO2HMZ3/q/03vjz9u2z1iPjhKEBdsOWN1YUVllpAAKxhAcfs4rNnp1FLlu1A+verm+Af7Gsg/QRgyxurCysstYAAWMO8jpnF58D0zK1v5by2nEXkWLdyTgC2vLG6sMJSCwiANczjmFV8Dk/P3DRAT+8Uk4lUAarJRKYAW95YXVhhqQUEwBrWcswoPkelZz7BdHZVgGo6uynAljdWF1ZYagEBsIY5jtnE59j0zBWgfqhVhC1vrC6ssNQCAmANazhmEp/jw7OUZXkKX3QcVcl5YtANrwB1wZY3VhdWWGoBAbCG1RyziE+b9MxtO5HKjqN5gOqZSJOALW+sLqyw1AICYA1bOmYQn2bpmdsG6PP97OrjMkBf3NBTOacAW95YXVhhqQUEwBpWOTY+Pi3TMzceSH+cZdnB/t7lS7P/r40UlitA22DLG6sLKyy1gABYw0rHRsencXrm1vfC/2F/MSGoQX4qQFtgyxurCysstYAAWMNmjo2NT/v0zM0nE3n56cEsPc9d+SL4iQgUoC7Y8sbqwgpLLSAA1rCx8TlJeuaaD9QPtYqw5Y3VhRWWWkAAqmHj4nOq9MynDND/V8OYzKGWtwyLBasLKWxUfE6Ynvl0AXp6TwPp7WGWdy7DosHqAgobEZ9ueBqnZ24YoA/fOTh48+7i1fMbmQLUHmJ5l2B1YYWlFhCAZ9jw+Jw+PXOzAH1xY975Pr/9qJxYWQFqD6+8K7C6sMJSCwhAM2xwfG4kPXOrAH11fTF8qUjQMk3PGwykfyKE2F3q8RmzXCs9pxLYk7UBWoygf3/+363ihqQsu2rwUCQdgbrQjg+WYHVhhfX5UHlIMrWSJiTDmkefvYVt6thzjskR6OmdauT8UZZdLPLT4PAzV4C2IZV3A6wurLD1H1mc1G00QjGGPXVP3vsJ22h4FlgF6PzW91l4nr9hc/iZK0DbYMrbBasLK2zdB7I6m1A0B2JYKz57ldjG0zM3CtBX16suo/Ja6N77NtIUoC0g5d0GqwsrbM37WZYmQRGGeeJzvWMp0jOfIkANpmFabNZqRdEQqsgHorx9YHVhha15f3cD9Kk3Ptc4lig98ykC1GIakWqzZmuKJXkVBUhf3gGwurDCut/OXDajKr1hofjscGyznUYu5gE6fvjnarNma4oFu9thhaUWEGBbDdvRAA2mZx5yLG165gpQP9jdDisstYAAW2pYKz83lqApDeuKT69jydMzV4D6we52WGGpBQTYUsN2MUC747PtGCE9cwWoH+xuhxWWWkCALTVs9wJ0/S2bDWGQ9MwVoH6wux1WWGoBAbbUsF0L0PXxWXcME54FVgHaRpOJ2LOleZCOLTVstwK0T3wuHUOlZ64A9YPd7bDCUgsIsK2GpcrPBIb1i8+5Y7T0zK1u5fzoZpufakZ6c7Y1D5KxrYbtSoCu6zmq0ZpiaTMK16FnIvnA7nZYYakFBNhawxLl52YN6x+fnE4jFwWoD+xuhxWWWkCArTXs7Ado+J4jF2565gpQP9jdDisstYAA22tYmvzcmGG94xOdnrkC1A92t8MKSy0gwDYbliA+N2TY077xSU/PXAHqB7vbYYWlFhBguw3beHxuxLAzlJ65AtQPdrfDCkstIIAMi2Ryw3rGZzs8oY4pQH1AG0t5EIsMi2Riw4amZ451TAHqA9pYyoNYZFgkkxo2KD6XyiYUNgIFqA9oYykPYpFhkUxo2Jj0zLGOKUB9QBtLeRCLDItkMsP6xGdnpxHUMfsAPX00Qk5zs1YrigbaWMqDWGRYJBMZ1iM+13W5Qx0zDtCH7xSziLz6yXsWDzZWgLooDyKRYZFMYtj6+OwxYAnqmGmAnt6bT8P06np23mBaUAWoi/IgEhkWyQSGrY3PfuM9oY6ZBuhRlp3/q/03vjz9uyy7MP4YVAHqojyIRIZFYm3Yuo73/qPloY5ZBuhJlr2fv7peTAT6YD+7NVqbAtRFeRCJDIvE1rA18dk/PXOsY5YBelQ8E34eoPlxdnGsNAVoC+VBJDIsEkvDOuOzFZ7r7tSEOmYYoKd39n69DNDn+5qR3h7lQSQyLBIzwzpv2YxOzxzrmGGAzqOzCtDqv1EoQF2UB5HIsEiMDOuKzyHpmWMdU4D6gDaW8iAWGRaJiWEx6dlfmYGwCTA9hS86jqrkPDHohleAuigPIpFhkYw3rOPgc3h65ljHLDuRyo6jeYDOwlSdSPYoDyKRYZGMNSwcn2PCs1Q2TthUWAbo8/3s6uMyQF/cyIoOpZEoQF2UB5HIsEjGGTZZeuZYx0wH0h9nWXawv3f50uz/a2OVKUDbKA8ikWGRjDAsePBpkJ451jHbe+H/sL94jotBfipAWygPIpFhkQw2LBCfoy57NpUNXnJSjCcTefnpwSw9z135YpyqarMWKxkEtLGUB7HIsEgGGjZ1euZYxzQfqA9oYykPYpFhkQwyzBufpumZYx2zHMb00U9XI5eev/sXGsZkjvIgEhkWyQDDfPFpnZ451jHzgfS+FwNRgLooDyKRYZFEG7aZ9Myxjk0VoLoXfgqUB5HIsEgiDWvH5yThWSozW5MpNgH66nrWQnci2aM8iESGRRJlWCs+J0vPHOuY0RHoSTtANR+oPcqDSGRYJBGGufE5ZXrmWMeMAvT0v9y8+e7+3uWbC372+XhtClAX5UEkMiyS3oY58TlxeuZYx6a6BmqBAtRFeRCJDIukn2HNjvdpOo1ayiZZ62imGsZkgQLURXkQiQyLpI9h2TmYWwAAIABJREFUjfjcTHrmWMf0XHgf0MZSHsQiwyJZb1iS9Myxjum58D6gjaU8iEWGRbLGsFTpmWMd03PhfUAbS3kQiwyLpNOwdOmZYx3Tc+F9QBtLeRCLDIukw7Bwem5G2Ua2Eo2eC+8D2ljKg1hkWCQhwxKnZ451TM+F9wFtLOVBLDIsEr9hwfjcpLINbisCPRfeB7SxlAexyLBIfIYB0jPHOqbHGvuANpbyIBYZFknLMEZ65ljHFKA+oI2lPIhFhkXiGEZJzxzrmJ4L7wPaWMqDWGRYJA3DOOmZYx3Tc+F9QBtLeRCLDItkZVjiTve2smRb7kTPhfcBbayJ86CchXDYortp2AiwuiphsPTMsY7pufA+oI01aR4sJ3IdsvAuGjYKrK5SWJp7jdYoS7z9AHouvA9oY02YB425sOMX3z3DRoLV9QSZnjnWMT0X3ge0sabLA+dpAtHL75xhY8HqYqZnjnVMz4X3AW0sBWgsCtA4qOmZYx1TgPqANtZkedB6oFXsCnbNsNEQdYHTM2c6lhsF6OlHN9uMn55eAeqiAI1EAdobVJe7B55jJSYB6nuqcaY7keyZKA88rRcrbBJd41GA9gSenjnPsQoFqA9oYylAY1GA9oGfnjnMsRW6BuoD2lgK0FgUoOsgX/ZsgHGsiQLUB7SxFKCxKEA7aacn1jCIYy0mCNA/DVfjbNZqRdFAG0sBGgs2D9LraoVneeyJNQzgmJcJnsqZZXsaSD8N6oWPBJsHiXW10/NppYtqWGrHQtg+lfPOcg+8avBcYwWoiwI0EmwepNQVTM8cbBi1xCwDtMjPvcv/8Nl/fXc/M5jNTgHaQnciRYLNg2S62uH59Gntbaxh1BKznQ80+8v5gefpJ5meyjkBCtBIsHmQRtea9MzBhlFLzHZG+tUcTEeaUHkCNBtTJNg8SKDLE55ufIINo5aY6TORapMo66mcU6D5QCPB5sHGdfVJzxxsGLXEzB8q53sxEAWoi2akjwSbB5vV5UtPX3yCDaOWmPlz4Sue7+uhcvZgyxurCytsc5vqn5452DBqiZk/VM7z81AUoC7Y8sbqwgrb0Ha86RmMT7Bh1BIzHsa0GP55bDCXiAK0Bba8sbqwwjaxEX96dsQn2DBqiVmewn9UjP+8/N5n/1T8/+b4WUEVoC7Y8sbqwgqbfAveTvfu9MzBhlFLzLQTyXNP9ZgDUQWoC7a8sbqwwqZdfSA918Un2DBqiSlAfUAbi1veWF1YYdOt2jdavmd8gg0jlJhvmIqms/MBaCwv2PLG6sIKm2i9o9IzBxuWvsT8A6UVoD6SN1YAbHljdWGFTbHScHr2jE+wYalLLHSrngLUB7WKsOWN1YUVZr7GjvTsHZ9gwxKXWHCyCOMA/eNnS36rgfTmYMsbqwsrzHZ1NumZgw3biQD9w75R91G12bErGAy1irDljdWFFWa4ro7wjIxPsGFpS6zVOb58xzJAT8z636vNjl3BYKhVhC1vrC6sMKsVdaZnbHyCDduBAD29k+3d/dOSRyP0VpsdvYahUKsIW95YXVhhJmtx0zMfl5452LCkJeYZn7mUZToOdPwkyo3Nmq4tBmoVYcsbqwsrbPwqWuk5Pj7Bhu1EgBrc/97YrOnaYqBWEba8sbqwwsYt3uo0ctJzYHyCDduBAD29owCdGGx5Y3VhhY1YdrL0zMGG7UCA5sc6hZ8YbHljdWGFDV1wbXqOiU+wYbsQoI1nehigAHXBljdWF1bYoKXWp+e4+AQbtgO98Hn+4np2/uaCMRPZVZsdu4LBUKsIW95YXVhh8YtMn5452LCdCNB79U1oHKg92PLG6sIKi/x8OzxtT92XuqiG7cKdSMeZAnRasOWN1YUVFvNhX3ra9Rs1dVEN24EALQfSjz5vr2/WcF1xUKsIW95YXVhhvT+5wfTMwYalLrFAfk72XHgLFKAu2PLG6sIK6/cxb3paDJgP6qIalr7EfPGpgfR+kjdWAGx5Y3Vhha3/iKfTqGTC+AQbRiixdnxqIL0fQGN5wZY3VhdW2Jr3e6WneXyCDaOWmG0n0rXRehqbNV1bDNDG4pY3VhdWWMd7rfBcpOfk8Qk2jFpits+F33t/tKD6Zi1XFgW0sbjljdWFFRZ6I5ye0567L3RRDaOWmO1z4bNs77IG0k8HtryxurDCvL/tm55TxSfYMGqJTfdYY40DtQdb3gN1+a7Km7JNhoXDc2PxmcCw3iUAbUkFqA9oY21VHqzHPy7ElK0xrCs9N3LuvtC1WcMiSgDaknoqpw9oY21PHvQgNDLZlC0xrCM8NxqfGzYsqgSgLakA9QFtrG3Jgz4E740zZRsM65+ek8fnZg2LKwFoSypAfUAbayvyoCcK0BJUeuYK0GiMA/TlpwdZtnfwnueJcv/r3x0evvWLr+YvXv/m9uGh90V9sz2kTQO0sfh50JvMZQpVdMMi0nMz8blRwyJLANqStgG6mo+pNaT+d4clb/1j8eL7D8oXP/x960Vjsz2kTQO0seB5EIMCNCY9NxWfCtBoTAO0yM9zl2++e6mdoM8O3/qbPP/u43lO3j98+6vixdt/dl80NtvrG0wBtLHIeRBHa+eZKEGphnX1uadLz3yThsWWALQlLQP0+X524Yvypxd3subMTK8/Pvzb4v/Z0ebs/29vlzH6/QfF8WjjRXOzPaRNA7SxsHmgAI2hMz0TnbovUIBGYhmgR9mFxd1Hp3eyi/W3vv+gOkO/f/jLPP/m8Mfli29aL5qb7SFtGqCNxcyDAgVoTzrHe6ZOz1wBGo3pbEy1o87n+xe8t3KWAXp/fjg6O6//sfOiudke0qYB2li8PFigAO3DmvRM0evuogCNZKr5QAOTg5Yn6q8/rs7Wv7399p8bLxabq3giziievSe1pKlppafzfjM9k0jcKLtUAnYBWp6vK0DF7uw8JevSc+fi88lOlUDvZyLdWr44yXyn8M/KYUy1zPzh7xsvmp/WKbwL64y0hoYxdeA7c2/qApy7V2gYUyQb6kSa8+z2W8X1znVHoMvN9pA2DdDGYuSBD92JFCJw2bOmi5Oeue5EisZ4GNP5z8ufvr6ReR4w9001jF4BOpT0eRBAAeol3Gm00JW8291BARqJ+UD67ODgoPiv/XSP3x0uRnqqF34gZyhAz/5sTJ1d7qUuWnrmmo0pGttbOR/sV260n+3x+v7hjxbXOBdDPqtxoL9s/LK22R7SpgHaWGcqQM/2fKBrBizNDEt5v1EYzQcaifFkIqcP350dgV6+2+5Aul+7VVN3Ig3kbAXoWZ2Rft1wzwJkeuaakT6aTU1n9039VvfXHx/+aHn7e+NFc7PxmzEC2lhnLkAnZ/OGbXN65uAKo5bYhgK0mnGpoLjS+V19AqbvNBtTX7DljdW1WWFbnp45uMKoJWYYoC+rSUCfv9k+g3922AjQ/LvfzH76RXXI2XhR32wPadMAbSxueWN1bVDY9qdnDq4waomZBeiLG4uO92NfH9IQFKAu2PLG6tqUsDORnjm4wqglZhWgRf97NXT+E/8opngUoC7Y8sbq2oiwHuEJ7XRvga0waokZBejJLDOrQfR5fnpv9upW6zPRKEBdrMvbrBd8Vwzz0Cc92wefO2zYQKDCbAK0eCR8/Zjz2OKx8ArQFrblbTgOczcM89AnPX03a+6sYYOBCrMJ0FlgNuYOOb1jcRKvAHWxLG/TO4F2wbA2Q9Mz31XDxgAVZhKgRV42T9lPMu9sTHEoQF0My9v2XvQdMMylV3rm3vTMd9KwkUCFmQTo7AzemTvk+b7BObwC1EUBGslUedAvPYPxuXuGjQcqzCpAnbhs/2YAClAXu/LOXMat7uwbVqNnenbE544ZZgJUmALUB7SxFKCx2OdBr073fO0sn7tjmBVQYVbXQNun8LoGao9Zebfyc2SCnnnD5hilZ74zhhkCFWbTC3/kdrofZ74Z6SNRgLooQCMxzAM3PHumZ2DE/A4YZgxUmE2Aup3uGsY0DQrQSKwMG5qewRuOzrph9kCFTTOQ/kgD6SdBARqJhWGt8OzZbdR5u+ZZNmwaoMIMb+W8ujgGffFhZnIzvALURQEayWjDJknP/AwbNhlQYVaTiZSPQzr/95999tmnN4ofx18BVYC2US98JOMMmyo985VhBt6bogCNxGw6uz/s1/bGvZ9baFOAuihAIxlhWP/wHDJP3VyXkfuGKEAjsZtQuTxxn8fn1UcGyhSgbXQnUiRDDZs2PfO5YZYNYIUCNBLTR3p8/U8f3fzZZzbpmStA2yhAIxlkWP/wHD5J8hPrFjBCARrJph4qNwQFqItmY4ok3rBNpGeuAI0HKkwB6gPaWJoPNJY4wyJO3Mc+oOOJ9VVoIxSgkShAfUAbSzPSxxJh2AbTM1eAxgMVpgD1AW0sbnljdfUUttn0LIS18pORoNgKo5aYAtQHtLG45Y3V1UdYVHoaPVtTARoLVJgC1Ae0sbjljdW1VliK9MwVoPFAhSlAfUAbi1veWF3dwqLC0/S57grQWKDCFKA+oI3FLW+srg5h6dIzV4DGAxWmAPUBbSxueWN1hYQlTc9cvfDxQIUpQH1AG4tb3lhdXmGj0tMiPhWg8UCFGQfoy08Psmzv4D2T2zkVoC7Y8sbqagmL6zSaJj1z3YkUD1SYbYAeL8vBYDpQBWgLbHljdTWFQdIzV4DGAxVmGqBFfp67fPPdSzYJqgB1wZY3VldNGCc9c83GFA9UmGWAFo/i/KL86UXrMZ1DUIC6YMsbq2shDJWeueYDjQcqzDJAj1ZPlju9o6dyTgC2vLG6SmGR6TlBp3tbWPU/Kz7BFUYtMcMAPb1TO+p8vq/nwtuDLW+srieRXe4bSc8cbRgUqDDDAH11vfYkzsaLgShAXbDlTdXFTM+caxi2wqiOKUB9QBuLW95MXdT0zKmGgSuM6pjpKXx2a/niJNMpvD3Y8gbqAqdnjjSsBFthVMfUieQD2ljc8qbpikzPTccnz7AF2AqjOmY8jOn85+VPX9/QMKYpwJY3Stc06WnbX97LsARd9NgKY5XYCvOB9NnBwYHRrUgKUBdseXN0Oem5VljPY0/rEZs9DEsySBRbYaASa2B7K+eD/arN994fqavcrME6hgFtLG55Q3S1Ot3XGNb3zN3+nqG1hiW6TQlbYZQSczGeTOT04buzI9DLd0d3IJWbtVjJIKCNxS1vgi7fkKUuw/pf95zgrvV1hqW6UR5bYYgS86Dp7HxAG4tb3ql1ueG5uPAZNiym20gBCgAqzHIY00c/XR14Pn/3LzSMyRxseSfVFUrPPGhYXKd75mKgeY1hU2yyF9gKo+6TGkjvA9pY3PJOp6sjPXO/YdFDlhSgBKDCpgrQ5/sKUHuw5Z1IV3d65h7DBgz4bIWZRZx1GzbJJvvpolYYdZ+0CdBX19strjuR7MGWdwpda9Mzdw0bNlxeAYoAKszoCPSk3eK3Wh+KRQHqgi3vjevqEZ4FNcOeDotPBSgEqDCjAD39Lzdvvru/d/nmgp99Pl6bAtQFW96b1dUzPfOVYYPTM1eAQoAKm+oaqAUKUBdseW9QV//0zOeGueEZe6e7AhQBVNhUw5gs2JYAHVzg0Qtiy3tDuvpc9mzwZGx6FkwRZuqFjwUqTAPpfcQ01uAaH7Agtrw3oSs6PUcfe1YoQAlAhSlAffRvrMFVPmhBbHlPrasVnhtLz4IJwkx3IsUCFaYA9dG7sQbX+bAFseU9qa606ZkrQBFAhSlAfShAI5lOl0F6jhdhH2aajSkWqDAFqI++jZW59N3AwAWx5T2RrvjwnOqJ7tZZpvlAY4EKU4D6UIBGMoWuAenZutfI0DDbLOula+PxCa4wBWg8+ABtxWDfch+6ILa8zXUNCE/fnZq7Y5gRWMOojilAfShAIzHWZZOe+e4YZgbWMKpj9gF6+miEnOZmrVYUjQI0EktdZumZ74hhlmANozpmHKAP38myN7589ZP3LO5JUoC2dEGryK66LdMz3wXDjMEaRnXMNEBP7xVRMAvQ69l5g9viFaAtXdAqMqru8enpdrpvdydSArAVRnXMNECPsuz8X+2/8eXp31lMB8oPUPXCLzDQZZ+euaFh0S28BmxDUoVRHbMM0JMse7+ak+nB/k7MB6oArRira/x4T/+ATyPDBrVxJ9iGpAqjOmYZoEfZteWkdsfZxbHStiBAdSdSxShdU6VnbmXY4FYOg21IqjCqY5bT2d3Z+/UyQHfkmUgK0DmDdcWPlm+FZ9fNRgrQSLAVRnXMfELlKkB35amcg/esQQtiy3uYronTMzcybOD1lk6wDUkVRnVMAepD84FGMkDX9OmZK0CjwVYY1THTU/ii46hKzpMdeirn4J0qekFsecfqMkjPfroMDGvl5/SP9EgHtsKojll2IpUdR/MAnYXpTnQibRhseUfp2lR65grQaLAVRnXMMkCf72dXH5cB+uJGVnQojUQB6oIt7/66xne5R+lSgMaBrTCqY6YD6Y9ntXWwv3f50uz/a2OVKUDbYMu7p67NpmeuAI0GW2FUx2zvhf/D/qLEDPJTAdoCW959dG3uxL2mSwEaB7bCqI4ZTyby8tODWX2du/LFOFXVZi1WMghoY3HLe62uFOmZqxc+GmyFUR3TfKA+oI3FLe9OXdGdRgPGK4V0KUDjwFYY1TEFqA9oY3HLO6wrYXrmuhMpGmyFUR0zDtA/frbktzszDnRzYMs7oCs2PdvhOfKhcArQSLAVRnXMNEBXfUhZMS3oWG0KUBdseft0jU9PA12ajSkObIVRHTOezk4BOinY8m7pIqRnrvlAo8FWGNUx21s59+7+acn4RyMpQF2w5d3UNfqyp50uO8Ms41MVFg9UmOlkIgaTKDc2a7q2GKCNxS3vmi5OeubbYRgKrGFUx8xnYzJEAeqCLe+FrrHpaa6LbhgNrGFUx0xP4RWgE4Mt71IXLT1zumFAsIZRHbOdjUmn8NOCLe8no4d7TqQLa1hqAQGwhlEdswzQV9cNpmCqb9ZyZVFAG4ta3tD0zLGGqcLigQozHQf64np2/uaCn2ogvTnA8m6FZ/SApSnVAQ2bg9VFFUZ1zDRA72kc6LTQypudnjnPsCVYXVRhVMdsr4EqQKcFVd7t9OweJtkKz6nTM4cZVgeriyqM6pj1QPrR5+31zRquKw5oY4HKux2e3QPNU6RnTjLMAauLKozqmOk4UNs+JAVoC0h5B9IzFKDt8NxMeuYYw9pgdVGFUR3TQHof0MZilHcoPP0Jmi48CxCG+cDqogqjOqaB9D6gjQUo7870dAM04aFnRXrDAmB1UYVRHbPtRLJ4ElJts6ZriwHaWKnLu9Xj3srPWoImD88CbB5gdVGFUR2zDNDTO3vvjxZU36zlyqKANlbS8vaNVwoFaPpDzwpsHmB1UYVRHbM8hf/o3Szbu6yB9NORrLwDoz29AUoJzwJsHmB1UYVRHbOdzk7jQKclTXmHB8u34hNz6FmBzQOsLqowqmMKUB/QxkpR3uH0zN0AhYVnATYPsLqowqiO7fJTOf3jbgqgjdWvtcJfK5bWeE//toiHnhXYPMDqogqjOra7AeofeTMH2lh9Wqvra0WxPj2XW2OGZwE2D7C6qMKoju1qgPqH3iyANtb61ur+Wv3plZ7lBqGHnhXYPMDqogqjOmYSoKcfFX3us3/roHvhW13HTaCNtba11nytnvROT3Z4FmDzAKuLKozqmEmAvrpedBltUyeSAjTEmPTc0JlJBNg8wOqiCqM6tpsBmrk470Mba11rrftaaxkTnobP/LUDmwdYXVRhVMd28xqoAtRDz/T0hOfTEdcLpgWbB1hdVGFUx3YyQFtB4+7+0MZa01prv1YHw9NzLqz3hjYLNg+wuqjCqI4pQBWgPdOzq8toOw1LCFYXVRjVMcNroOYoQF0mCNCenUZrOty307CEYHVRhVEdU4DucoD2S88eo5W207CEYHVRhVEdQwfok4nwJM1Um9okkV+rlZ7eT7XDcxLtQpxdztoRqHrh+x179jj0XAjr+w02DPaAaqGr54WWSIavFWsYtcTQR6D2q6zY8QAdmJ4da9xSw9Ix19X3UkscY9aKNYxaYrsZoLt8J9LAQ8/ulW6rYckodPU8WYhk3FqxhlFLTAG6UwE6/NCze6fcVsOS8cRq4gKXkWvFGkYtMasAbUO+lXM3Z2PqkZ7eq5499smtNSwVCtBYoMJ2NUB3bj7QQenZXOPZNCwNT8ZPXOBl7FqxhlFLzCpAVw+T24rp7ErCBQZtrKEz0vfoNOrocFeAToACNBaosB29BroGaGMNKu+16enJzvpoJc/JhUdYvK6NgM0D36Bdg9WOXivWMGqJKUB9QBsrvrzXpKcvO92xngrQKVCAxgIVpgD1AW2syPLuTs8+4VmgAJ0CBWgsUGEKUB/Qxoopb5v0zBWg06AAjQUqTAHqA9pYvcvbKjwLFKBToACNBSpMAeoD2lj9Wis6Pdesr88uudWGpUC98LFAhdk8lfOPn/129KAlz2btV9kTaGP1aK2u9IzPzhIF6AQoQGOBCtvJGenXAm2sda0Vl569t9pjl9xSw9KhO5FigQpTgPqANlZna7npWdt7Bh56LlCA2qMAjQUqTAHqA9pY4dbypOdi7xkXniXrd8mtMyw1mo0pFqgwBagPaGMFWsufngUjDz1XrNsjt8swAJoPNBaoMAWoD2hj+VorlJ4Duts76d4jt8gwBgtd9vE5bq1Yw6glpgD1AW2sVmv509OXnePSc72wSdc+HGweYHVRhVEdU4D6gDZWo7Xc8PxBnig8S2GTb2EY2DzA6qIKozqmAPUBbaxVa7XTMw/dYjR9euZbYBgNrC6qMKpjClAf0MaqWisiPTcmbFMbigSbB1hdVGFUxxSgPqCNVbSWLz292TlN90RI2KY2FAk2D7C6qMKojilAfUAby5eeweyMz88RkTvQsMlDHpsHWF1UYVTHFKA+kI3VCs/wgeeQAB01JnGQYdOMgmyAzQOsLqowqmMKUB+8xnLT03/FM8uG5ufIu2IGGDZyi/3A5gFWF1UY1TEFqA9YY/VLz3zEjdDDk3dOvGFjt9gPbB5gdVGFUR1TgPogNVaf9Fx+2OQAVAE6PVhdVGFUxxSgPiiN1eo06grPOYMiKXOJFRpt2Ogt9gObB1hdVGFUxxSgPhCNtT49/csNyCMF6KbB6qIKozqmAPWRvrHWpafpxlppFj+R+ca32FNX+pb0g9VFFUZ1TAHqI3FjrUlP8+0pQDcOVhdVGNUxBaiPlI3VmZ7TCFOAbhysLqowqmMKUB/JGqsjPEtdCtBIXdDdjhoHXMOojilAfaRprHB6LnUpQCN1QXc7ahxwDaM6pgD1kaKx1qZnPl15j04z9cJHgtVFFUZ1TAHqY+ONFUpPV5cCNA5sHmB1UYVRHVOA+thsY/VMz3zC8h6bZroTKRKsLqowqmMKUB8ba6ynTwPpGdClAI0DmwdYXVRhVMcUoD420lhPA+nZpWsyYSPTTLMxRYLVRRVGdUwB6mPqxiqD0tPpvlbXhMJGhZnmA40Eq4sqjOqYAtTHhI1VnaK307OXrkmraESYDdQ1cXyC8wCriyqM6pgC1MdEjRUIzx/01wWtImp1y7BYsIZRHVOA+rBvrGXX+vD0zMHljdWFFZZaQACsYVTHFKA+TBurdjvmqPTMweWN1YUVllpAAKxhVMcUoD6sGqsxidLY9MzB5Y3VhRWWWkAArGFUxxSgPgway5m/0yA9c3B5Y3VhhaUWEABrGNUxBaiPcY311MUkPEtd0CqiVrcMiwVrGNUxBaiP4Y3VCk+79MzB5Y3VhRWWWkAArGFUxxSgPgY1Vjs7bdMzB5c3VhdWWGoBAbCGUR1TgPqIbSxfdhpd9mzqglYRtbplWCxYw6iOKUB9xDSWNzzb6Wlyxw22vLG6sMJSCwiANYzqmALUR8/G8mdnOzx/YHXLIra8sbqwwlILCIA1jOqYAtTHelMC2RlMT5sExZY3VhdWWGoBAbCGUR1TgProNiWUnZ4naq7SUwGaAhkWCdYwqmMKUB9BU8LZ6UlP+6dWYMsbqwsrLLWAAFjDqI4pQH34TAmetJfvrk1PBWgKZFgkWMOojilAfTRNCUbnYg5P33glBSgBGRYJ1jCqYwpQH5Up4eSszX8cGu3ZIz87Y9X3ZsLy7v4TsHFdPf8iYfMAq4sqjOqYAtTHk87srE0eH0rPvEeAdh6Z+t9MVt7rjqI3rKv3QT02D7C6qMKojilAW/TMzvZlT2c93fnZL1udNxOV9/oLERvVFXFdBJsHWF1UYVTHFKB1+mbn+vTM1wTosDfTlHf3X4KSTerqIWcJNg+wuqjCqI4pQBf0jc68V3oW9D4AVYAay1mCzQOsLqowqmMK0HzdCCWHnulZEtzLM5d+byYp706tFRvU1UfOEmweYHVRhVEd2/UAjcrOuPQsCeziCtBJ5SzB5gFWF1UY1bHdDdCuM3a/KbHhGaYVAvUY6HgzRXl3al0KY8lZ6YLudtQ44BpGdWwHA7RHR5HHFLv0zBWgU8tZ6YLudtQ44BpGdWynArQrOhsn7S1TLNMzV4BOLWelC7rbUeOAaxjVsV0J0N7ZWdI0xTg9cwXo1HJWuqC7HTUOuIZRHduBAI3LzpKaKfbpmStAp5az0gXd7ahxwDWM6tiZDtAB0TlnYcok6VnQGQLhN9ULr174acEaRnXsbAbo4OSsKE2ZLD1zBejEcpZg8wCriyqM6tgZDNBR0TnniW2nu4fOEAi+qTuR+slZgs0DrC6qMKpjOxOgMZqnTs9cATqtnCXYPMDqogqjOrYLAdpjR1vhhucU6ZkPnapJszH1k7MEmwdYXVRhVMfOYIDmbnT22tVKNpSegycL1Xygc3q3KTYPsLqowqiOndEAzSM7Gwo2lp79xPl+rRnpF/T8k4jNA6wuqjCqY2cxQEtaEdW9v20yPYc/MAlb3lhdWGGpBQTAGkZ1TAGae9Jz6sZSgG4IGRYJ1jD6KJuiAAAM+0lEQVSqYwpQ36GnAjQSrC6ssNQCAmANozq24wEaOHFXgEaC1YUVllpAAKxhVMd2OUDDlz0VoJFgdWGFpRYQAGsY1bEzG6DrOro7O43sGisQjj3y0/drbHljdWGFpRYQAGsY1bGdDNC1Xe5WjRXMx7UB6n8DW95YXVhhqQUEwBpGdezsBmjoZp8+A5ZsGqsrIbvzM/QetryxurDCUgsIgDWM6thuBWgrPAPDPU0aqzMjh72JLW+sLqyw1AICYA2jOnaGA9Q9juubnrkCNBqsLqyw1AICYA2jOnaWA7R+JTEiPXObxspcDN7EljdWF1ZYagEBsIZRHTvbAVqFUVR4FihAI8HqwgpLLSAA1jCqY2c9QIfd5W7QWK0U7PnYI9wzkXqB1YUVllpAAKxhVMfOeoDGh2eBAjQSrC6ssNQCAmANozp2pgN0WHrmCtBosLqwwlILCIA1jOrY2Q3QwemZK0CjwerCCkstIADWMKpjZzRAx6RnrgCNBqsLKyy1gABYw6iOncUADaenE1VNam+qFz4SrC6ssNQCAmANozp2BgO0Oz5DEdp4UwEaCVYXVlhqAQGwhlEdO9sBWv91MK88b+pOpEiwurDCUgsIgDWM6tgZDtDmb6MSSwEaCVYXVlhqAQGwhlEdO4MBmnu7jTYfoFHHvP3exJY3VhdWWGoBAbCGUR07kwHq+V3m0vnm5POBDnwTW95YXVhhqQUEwBpGdewsBqiPNAHav9+/35vY8sbqwgpLLSAA1jCqYzsSoK2I7D0qEwW2vLG6sMJSCwiANYzqmAJUAWoAVhdWWGoBAbCGUR1TgCpADcDqwgpLLSAA1jCqYwpQBagBWF1YYakFBMAaRnXszAWoP//GBmiCVPVtElveWF1YYakFBMAaRnXsjAVodwKGIrL15pPAwvGChuLfJLa8sbqwwlILCIA1jOrYmQrQjoPIEQHaeWg6DaFNYssbqwsrLLWAAFjDqI6dpQDtiMgRdyJ1LjgNwU1iyxurCysstYAAWMOojilAfW8qQCPB6sIKSy0gANYwqmOEAH39m9uHh7/4qr3ZuNVkLsPffNJzwWkIbxJb3lhdWGGpBQTAGkZ1DBCg339wWPDD37c2G7ceBWg6sLqwwlILCIA1jOoYIEDvH779Vf7dx4dv/9ndbNRqWrFj8QyNFGNEOzaJLW+sLqyw1AICYA2jOpY+QL+9XR57fv/BW//objZqPQrQhGB1YYWlFhAAaxjVsfQB+s3hj6v/f+luNmo9CtCEYHVhhaUWEABrGNWx9AF6//Bvy/+fVUFa22zUehSgCcHqwgpLLSAA1jCqY8kD9PXH1an7t7cXF0H/+YInMXhyx+DNzgWnIcEmhRDTsRUB2g4ekzcThJnyU4izxIYC1B3IpGFM7iaxJ1hYXVhhqQUEwBpGdYx4Cr/cbOSqOpMu6k3diRQJVhdWWGoBAbCGUR1TgPreVIBGgtWFFZZaQACsYVTHkgeoWS98bvgcYc3GFAlWF1ZYagEBsIZRHUsfoIvxn2PHgZZ0Bl3/NzUfaCRYXVhhqQUEwBpGdSx9gFrdiVTRGXR932yZsuH4DG0SW95YXVhhqQUEwBpGdSx9gL7++PBHFvfCWwJtLG55Y3VhhaUWEABrGNWx9AGaf2c0G5Mh0MbiljdWF1ZYagEBsIZRHQMEaP7db2b5+Qv3+FMB2gZb3lhdWGGpBQTAGkZ1jBCgwc1uZjMeoI3FLW+sLqyw1AICYA2jOqYA9QFtLG55Y3VhhaUWEABrGNUxBagPaGNxyxurCysstYAAWMOojilAfUAbi1veWF1YYakFBMAaRnVMAeoD2ljc8sbqwgpLLSAA1jCqYwpQH9DG4pY3VhdWWGoBAbCGUR1TgPqANha3vLG6sMJSCwiANYzqmALUB7SxuOWN1YUVllpAAKxhVMcUoD6gjcUtb6wurLDUAgJgDaM6pgD1AW0sbnljdWGFpRYQAGsY1TEFqA9oY3HLG6sLKyy1gABYw6iOKUB9QBuLW95YXVhhqQUEwBpGdUwB6gPaWNzyxurCCkstIADWMKpjClAf0MbiljdWF1ZYagEBsIZRHVOA+oA2Fre8sbqwwlILCIA1jOqYAtQHtLG45Y3VhRWWWkAArGFUxxSgPqCNxS1vrC6ssNQCAmANozqmAPUBbSxueWN1YYWlFhAAaxjVMQWoD2hjccsbqwsrLLWAAFjDqI4pQH1AG4tb3lhdWGGpBQTAGkZ1TAHqA9pY3PLG6sIKSy0gANYwqmPoABVCiDOAAlQIIQaSJECFi9sQYg0yLBIZFskgwxSgiVB5RyLDIpFhkShAtwmVdyQyLBIZFokCdJtQeUciwyKRYZEoQLcJlXckMiwSGRaJAnSbUHlHIsMikWGRKEC3CZV3JDIsEhkWiQJUCCE2iQJUCCEGogAVQoiBKECFEGIgClAhhBiIAlQIIQaiABVCiIEoQIUQYiAK0BT8r393ePjWL75KLWO7+Pb2239OrWFreP2724eH//JvUsvYHr6b7ZKHA3ZJBWgCfndY8tY/phayTbz++FAB2pfvPpiX2F+nFrItfHt74C6pAN08zw7fmh0afPfx4Q9/n1rKFvHNoQK0L7M/Nj/6Kn/93/Q3uifFX+evil0yusQUoBtn1lh/W/z//Qfz/0UfikMEBWhPnlV/m785/HFqKdvBt7dLw77/IPovjgJ043z/QXXkef/wl4mlbA+zvzr/XtdAe7L4Ey368mz+l2aAcQrQdChA+3P/8MfqROrL8k+06ImOQLeQAa21szybnb4rQPtSOPU//83h4Y/+c2ol28LyGmj0JQ8FaDJ0gao35d8aBWhfZk79Zt4Lr1Ocnryej4z56+gKU4Cm4pm6SHtTXuxQgPbl2TwKZqGgEuvJt/97GaA/+ip2QQVoIp7dfksX+nvyTdn/rgDty7PFoed9neT049vbQ//iKEDT8I0ODnrz7e3SKwVoXyrDZFlv7g/+i6MATYJOriL45nCJepf7UPUp134Qnbz+ePBfHAVoAl7fP/yRCrs3CtBIlnnwTPce9EIBul3cV10PQOejvVmciWqkcU90Cr9NfKP8HIICtDff3i4mFlIvfG+eDR62oADdON9/sDwjVR9pBArQ/jyrJhfSQI+eLC4TRR+xK0A3zrNDBegQFKARfPebWYT+269Sy9ge/r9iPtABhilAhRBiIApQIYQYiAJUCCEGogAVQoiBKECFEGIgClAhhBiIAlQIIQaiABVCiIEoQIUQYiAKUCGEGIgCVAghBqIAFUKIgShAhRBiIApQAefhh/tZlr159/H85avr2bW0goRYogAVaB5eyir23i9/oQAVIBSggsxxVqMMTgWoAKEAFWCK/Dz33qPZT1+/MzsG/XWuABUoFKCCy/P9LLtaXfsswvTCYwWoQKEAFVyOsuxi/VVxCKoAFSAUoALLLCvLs/aK52/+7It5gJ5+eqk4tV8cm35d9tMfzF+f3sluPbwxe7tY8mFx4n/18XH2xpflJ18Un9y78sWGv4g4syhABZaTxgFoxSxAr1yfdypdmCfmvUUn0/kiJWcBerN4MYvM2Y8lb3xYBeiyS+rqZr+IOLMoQAWW48xztj4L0GxvdrD54k717vH8OumLG/O4LVJzdtz64m55BWD2ThmwZYDOPlkcfL68pwQVRihABZZZ4N1q/bII0PK3s6SsOpUuLt4oXhcBOl/q+X6Vv8fzAJ29vrhYcf3SgBCDUYAKLKEAnZ+6zw4wi1g8WYThLDmL19V/5eLVD7NFip+Wr4vPtK8NCBGPAlRgCQXo8jhyEYhzjhYBemHRm3Sx/k49NY8WISzEKBSgAstJ4Bpo9ctagL78+rOPLmWLAL3ofHD+yVfX63c1NbNXiGEoQAWW5/vNA8U//P0jX4A+uFRPRQWo2CAKUIHFGQc6v/rpBuh8rNLBzb9/dNQK0MUp+yJAdeFTGKMAFVyOGwNB56Oa3AA9Xt7t6QSo5xqoLnwKYxSggkvjXvgH8/NuJ0BXsVj1ta9y0+2FP1oezypLhREKUAGmuHVor5iN6bS4KbNMzmCAHjvXQH3jQKuPnvi694WIRwEqyNyrd/yUtw8FTuG/vpHN57urnbkft+9EOn939olPMh2AChsUoALNH/aXM9L/vPyFG6CvblTvX/mkPK6sBejpUXWP/P/h3guv/BQ2KEAFm9P/+k5triXPMKZyaqa9K59X3eyNu4zaszFdKp+vtOHvIM4sClBx9tGNR2IiFKDirHK0OO7Ure9iKhSg4qxy3JqWSQhjFKDirDLLzeJRyKcP9nXjppgIBag4s5wsevDP6xkeYhoUoOLs8vLevNNdPUhiIhSgQggxEAWoEEIMRAEqhBADUYAKIcRAFKBCCDEQBagQQgxEASqEEANRgAohxEAUoEIIMRAFqBBCDEQBKoQQA1GACiHEQBSgQggxEAWoEEIM5P8H2Mt0eZHo4kcAAAAASUVORK5CYII=" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb56"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb56-1"><a href="#cb56-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot4c.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
</section>
<section id="conclusions-1" class="level2" data-number="6.5">
<h2 data-number="6.5" class="anchored" data-anchor-id="conclusions-1"><span class="header-section-number">6.5</span> Conclusions</h2>
<p>Plot 4B isn’t an improvement over the straight line model shown in Plot 4, and the associated <em>p</em> value for adding the quadratic term is <em>p</em> = 0.391.</p>
<p>So I’ll proceed assuming a linear relationship between charge and time to complete recovery.</p>
</section>
</section>
<section id="tcr-stratified-by-block-period-groups" class="level1" data-number="7">
<h1 data-number="7"><span class="header-section-number">7</span> TCR stratified by Block Period groups</h1>
<section id="plot-5-time-to-recover-vs.-bt-by-block-period" class="level2" data-number="7.1">
<h2 data-number="7.1" class="anchored" data-anchor-id="plot-5-time-to-recover-vs.-bt-by-block-period"><span class="header-section-number">7.1</span> Plot 5 (time to recover vs. BT, by block period)</h2>
<p>This plot gets at whether both BT (<code>bt</code>) and the block period (in minutes: <code>block_period</code>) are important parts of the model to predict <code>time_recover</code>.</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb58"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb58-1"><a href="#cb58-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcr, <span class="fu">aes</span>(<span class="at">x =</span> bt, <span class="at">y =</span> time_recover, <span class="at">col =</span> block_period)) <span class="sc">+</span></span>
<span id="cb58-2"><a href="#cb58-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb58-3"><a href="#cb58-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">formula =</span> y <span class="sc">~</span> x, <span class="at">se =</span> <span class="cn">FALSE</span>) <span class="sc">+</span></span>
<span id="cb58-4"><a href="#cb58-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span> block_period, <span class="at">labeller =</span> <span class="st">&quot;label_both&quot;</span>) <span class="sc">+</span></span>
<span id="cb58-5"><a href="#cb58-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb58-6"><a href="#cb58-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Recovery, by BT&quot;</span>,</span>
<span id="cb58-7"><a href="#cb58-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="st">&quot;Stratified by Block Period (minutes)&quot;</span>,</span>
<span id="cb58-8"><a href="#cb58-8" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;BT (mA)&quot;</span>,</span>
<span id="cb58-9"><a href="#cb58-9" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Recovery (seconds)&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img 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" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb59"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb59-1"><a href="#cb59-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot5.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stderr">
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
</div>
</section>
<section id="model-for-plot-5" class="level2" data-number="7.2">
<h2 data-number="7.2" class="anchored" data-anchor-id="model-for-plot-5"><span class="header-section-number">7.2</span> Model for Plot 5</h2>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb61"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb61-1"><a href="#cb61-1" aria-hidden="true" tabindex="-1"></a>m5 <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_recover <span class="sc">~</span> bt <span class="sc">+</span> block_period, <span class="at">data =</span> tcr)</span>
<span id="cb61-2"><a href="#cb61-2" aria-hidden="true" tabindex="-1"></a>m5t <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m5, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb61-3"><a href="#cb61-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb61-4"><a href="#cb61-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb61-5"><a href="#cb61-5" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m5t, <span class="at">digits =</span> <span class="dv">3</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: left;">term</th>
<th style="text-align: right;">estimate</th>
<th style="text-align: right;">std.error</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">conf.low</th>
<th style="text-align: right;">conf.high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">(Intercept)</td>
<td style="text-align: right;">-62.263</td>
<td style="text-align: right;">38.727</td>
<td style="text-align: right;">0.116</td>
<td style="text-align: right;">-140.475</td>
<td style="text-align: right;">15.948</td>
</tr>
<tr class="even">
<td style="text-align: left;">bt</td>
<td style="text-align: right;">76.910</td>
<td style="text-align: right;">20.888</td>
<td style="text-align: right;">0.001</td>
<td style="text-align: right;">34.727</td>
<td style="text-align: right;">119.094</td>
</tr>
<tr class="odd">
<td style="text-align: left;">block_period2</td>
<td style="text-align: right;">54.000</td>
<td style="text-align: right;">35.039</td>
<td style="text-align: right;">0.131</td>
<td style="text-align: right;">-16.762</td>
<td style="text-align: right;">124.762</td>
</tr>
<tr class="even">
<td style="text-align: left;">block_period3</td>
<td style="text-align: right;">142.000</td>
<td style="text-align: right;">35.039</td>
<td style="text-align: right;">0.000</td>
<td style="text-align: right;">71.238</td>
<td style="text-align: right;">212.762</td>
</tr>
</tbody>
</table>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb62"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb62-1"><a href="#cb62-1" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m5) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb62-2"><a href="#cb62-2" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb62-3"><a href="#cb62-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: right;">r.squared</th>
<th style="text-align: right;">adj.r.squared</th>
<th style="text-align: right;">sigma</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">df</th>
<th style="text-align: right;">df.residual</th>
<th style="text-align: right;">nobs</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: right;">0.4249311</td>
<td style="text-align: right;">0.3828529</td>
<td style="text-align: right;">95.95747</td>
<td style="text-align: right;">4.15e-05</td>
<td style="text-align: right;">3</td>
<td style="text-align: right;">41</td>
<td style="text-align: right;">45</td>
</tr>
</tbody>
</table>
</div>
</div>
</section>
<section id="what-is-the-effect-of-adding-block_period-to-a-model-with-just-bt" class="level2" data-number="7.3">
<h2 data-number="7.3" class="anchored" data-anchor-id="what-is-the-effect-of-adding-block_period-to-a-model-with-just-bt"><span class="header-section-number">7.3</span> What is the effect of adding <code>block_period</code> to a model with just <code>bt</code>?</h2>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb63"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb63-1"><a href="#cb63-1" aria-hidden="true" tabindex="-1"></a>m5a <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_recover <span class="sc">~</span> bt, <span class="at">data =</span> tcr)</span>
<span id="cb63-2"><a href="#cb63-2" aria-hidden="true" tabindex="-1"></a>m5at <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m5a, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb63-3"><a href="#cb63-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb63-4"><a href="#cb63-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb63-5"><a href="#cb63-5" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m5at, <span class="at">digits =</span> <span class="dv">3</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: left;">term</th>
<th style="text-align: right;">estimate</th>
<th style="text-align: right;">std.error</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">conf.low</th>
<th style="text-align: right;">conf.high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">(Intercept)</td>
<td style="text-align: right;">3.07</td>
<td style="text-align: right;">38.267</td>
<td style="text-align: right;">0.936</td>
<td style="text-align: right;">-74.102</td>
<td style="text-align: right;">80.242</td>
</tr>
<tr class="even">
<td style="text-align: left;">bt</td>
<td style="text-align: right;">76.91</td>
<td style="text-align: right;">24.204</td>
<td style="text-align: right;">0.003</td>
<td style="text-align: right;">28.099</td>
<td style="text-align: right;">125.722</td>
</tr>
</tbody>
</table>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb64"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb64-1"><a href="#cb64-1" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m5a) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb64-2"><a href="#cb64-2" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb64-3"><a href="#cb64-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: right;">r.squared</th>
<th style="text-align: right;">adj.r.squared</th>
<th style="text-align: right;">sigma</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">df</th>
<th style="text-align: right;">df.residual</th>
<th style="text-align: right;">nobs</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: right;">0.1901638</td>
<td style="text-align: right;">0.1713304</td>
<td style="text-align: right;">111.1924</td>
<td style="text-align: right;">0.0027494</td>
<td style="text-align: right;">1</td>
<td style="text-align: right;">43</td>
<td style="text-align: right;">45</td>
</tr>
</tbody>
</table>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb65"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb65-1"><a href="#cb65-1" aria-hidden="true" tabindex="-1"></a><span class="fu">anova</span>(m5a, m5)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>Analysis of Variance Table

Model 1: time_recover ~ bt
Model 2: time_recover ~ bt + block_period
  Res.Df    RSS Df Sum of Sq     F    Pr(&gt;F)    
1     43 531641                                 
2     41 377521  2    154120 8.369 0.0008956 ***
---
Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
</div>
</div>
<p>Model 5a tells us that <code>bt</code>, by itself, has a meaningful effect (p = 0.003) on time to complete recovery across these 45 experiments. The equation is</p>
<ul>
<li><strong>time to recovery = 3.07 + 76.9 (<code>bt</code>)</strong>,</li>
</ul>
<p>with a 95% CI for the slope of (28.1, 125.7).</p>
<p>The ANOVA table tells us that adding the <code>block_period</code> category (1, 2 or 3 minutes) adds meaningful predictive value to Model 5a, with an associated <em>p</em> value &lt; 0.001.</p>
<p>Specifically this yields model 5, which is</p>
<ul>
<li><strong>time to recovery = -62.26 + 76.9 (<code>bt</code>) + 54 (<code>block_period</code> = 2) + 142 (<code>block_period</code> = 3)</strong>,</li>
</ul>
</section>
<section id="new-r-square-values-for-predicting-tcr" class="level2" data-number="7.4">
<h2 data-number="7.4" class="anchored" data-anchor-id="new-r-square-values-for-predicting-tcr"><span class="header-section-number">7.4</span> NEW! R-square values for predicting TCR</h2>
<p>Using the categorical version of <code>block_period</code>…</p>
<table class="table">
<colgroup>
<col style="width: 37%">
<col style="width: 20%">
<col style="width: 13%">
<col style="width: 13%">
<col style="width: 13%">
</colgroup>
<thead>
<tr class="header">
<th style="text-align: right;">Model</th>
<th style="text-align: right;">R-square</th>
<th style="text-align: right;">p for <code>bt</code></th>
<th style="text-align: right;">p for <code>block_period</code></th>
<th style="text-align: right;">p for model</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: right;">Model for TCR using <code>bt</code> and <code>block_period</code></td>
<td style="text-align: right;">0.425</td>
<td style="text-align: right;">&lt; 0.001</td>
<td style="text-align: right;">&lt; 0.001</td>
<td style="text-align: right;">&lt; 0.001</td>
</tr>
<tr class="even">
<td style="text-align: right;">Model including only <code>bt</code></td>
<td style="text-align: right;">0.190</td>
<td style="text-align: right;">0.003</td>
<td style="text-align: right;">–</td>
<td style="text-align: right;">0.003</td>
</tr>
<tr class="odd">
<td style="text-align: right;">Model including only <code>block_period</code></td>
<td style="text-align: right;">0.235</td>
<td style="text-align: right;">–</td>
<td style="text-align: right;">0.004</td>
<td style="text-align: right;">0.004</td>
</tr>
</tbody>
</table>
</section>
<section id="model-treating-both-bt-and-block_period-as-numeric" class="level2" data-number="7.5">
<h2 data-number="7.5" class="anchored" data-anchor-id="model-treating-both-bt-and-block_period-as-numeric"><span class="header-section-number">7.5</span> Model treating both <code>bt</code> and <code>block_period</code> as numeric</h2>
<p>If instead, we wanted to treat both <code>bt</code> and <code>block_period</code> as numeric rather than as categories, we’d have model 5B, as shown below.</p>
<div class="cell">
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb67"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb67-1"><a href="#cb67-1" aria-hidden="true" tabindex="-1"></a>m5B <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_recover <span class="sc">~</span> bt <span class="sc">+</span> <span class="fu">as.numeric</span>(block_period), <span class="at">data =</span> tcr)</span>
<span id="cb67-2"><a href="#cb67-2" aria-hidden="true" tabindex="-1"></a>m5Bt <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m5B, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb67-3"><a href="#cb67-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb67-4"><a href="#cb67-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb67-5"><a href="#cb67-5" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m5Bt, <span class="at">digits =</span> <span class="dv">3</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<colgroup>
<col style="width: 35%">
<col style="width: 12%">
<col style="width: 14%">
<col style="width: 11%">
<col style="width: 12%">
<col style="width: 14%">
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;">term</th>
<th style="text-align: right;">estimate</th>
<th style="text-align: right;">std.error</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">conf.low</th>
<th style="text-align: right;">conf.high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">(Intercept)</td>
<td style="text-align: right;">-138.93</td>
<td style="text-align: right;">47.753</td>
<td style="text-align: right;">0.006</td>
<td style="text-align: right;">-235.301</td>
<td style="text-align: right;">-42.560</td>
</tr>
<tr class="even">
<td style="text-align: left;">bt</td>
<td style="text-align: right;">76.91</td>
<td style="text-align: right;">20.716</td>
<td style="text-align: right;">0.001</td>
<td style="text-align: right;">35.103</td>
<td style="text-align: right;">118.718</td>
</tr>
<tr class="odd">
<td style="text-align: left;">as.numeric(block_period)</td>
<td style="text-align: right;">71.00</td>
<td style="text-align: right;">17.376</td>
<td style="text-align: right;">0.000</td>
<td style="text-align: right;">35.934</td>
<td style="text-align: right;">106.066</td>
</tr>
</tbody>
</table>
</div>
<details open class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb68"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb68-1"><a href="#cb68-1" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m5B) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb68-2"><a href="#cb68-2" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb68-3"><a href="#cb68-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: right;">r.squared</th>
<th style="text-align: right;">adj.r.squared</th>
<th style="text-align: right;">sigma</th>
<th style="text-align: right;">p.value</th>
<th style="text-align: right;">df</th>
<th style="text-align: right;">df.residual</th>
<th style="text-align: right;">nobs</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: right;">0.4205288</td>
<td style="text-align: right;">0.3929349</td>
<td style="text-align: right;">95.17043</td>
<td style="text-align: right;">1.06e-05</td>
<td style="text-align: right;">2</td>
<td style="text-align: right;">42</td>
<td style="text-align: right;">45</td>
</tr>
</tbody>
</table>
</div>
</div>
<p>This yields a p value = 0.001 for adding <code>bt</code> to a model with <code>block_period</code> alone, and a p value &lt; 0.001 for adding <code>block_period</code> to a model with <code>bt</code> alone. Again, we need both elements of the product.</p>
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</script><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class>
<div class="sourceCode" id="cb69" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb69-1"><a href="#cb69-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
<span id="cb69-2"><a href="#cb69-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> &quot;Time to Complete Block and Time to Recovery Analyses&quot;</span></span>
<span id="cb69-3"><a href="#cb69-3" aria-hidden="true" tabindex="-1"></a><span class="an">subtitle:</span><span class="co"> &quot;EMG Reflex Induction Recovery&quot;</span></span>
<span id="cb69-4"><a href="#cb69-4" aria-hidden="true" tabindex="-1"></a><span class="an">author:</span><span class="co"> &quot;Thomas E. Love&quot;</span></span>
<span id="cb69-5"><a href="#cb69-5" aria-hidden="true" tabindex="-1"></a><span class="an">date:</span><span class="co"> last-modified</span></span>
<span id="cb69-6"><a href="#cb69-6" aria-hidden="true" tabindex="-1"></a><span class="an">format:</span><span class="co"> </span></span>
<span id="cb69-7"><a href="#cb69-7" aria-hidden="true" tabindex="-1"></a><span class="co">  html:</span></span>
<span id="cb69-8"><a href="#cb69-8" aria-hidden="true" tabindex="-1"></a><span class="co">    toc: true</span></span>
<span id="cb69-9"><a href="#cb69-9" aria-hidden="true" tabindex="-1"></a><span class="co">    number-sections: true</span></span>
<span id="cb69-10"><a href="#cb69-10" aria-hidden="true" tabindex="-1"></a><span class="co">    date-format: &quot;YYYY-MM-DD kk:mm&quot;</span></span>
<span id="cb69-11"><a href="#cb69-11" aria-hidden="true" tabindex="-1"></a><span class="co">    self-contained: true</span></span>
<span id="cb69-12"><a href="#cb69-12" aria-hidden="true" tabindex="-1"></a><span class="co">    code-tools: true</span></span>
<span id="cb69-13"><a href="#cb69-13" aria-hidden="true" tabindex="-1"></a><span class="co">    code-fold: show</span></span>
<span id="cb69-14"><a href="#cb69-14" aria-hidden="true" tabindex="-1"></a><span class="co">    code-overflow: wrap</span></span>
<span id="cb69-15"><a href="#cb69-15" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
<span id="cb69-16"><a href="#cb69-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-17"><a href="#cb69-17" aria-hidden="true" tabindex="-1"></a><span class="fu"># R Setup</span></span>
<span id="cb69-18"><a href="#cb69-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-21"><a href="#cb69-21" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-22"><a href="#cb69-22" aria-hidden="true" tabindex="-1"></a><span class="co">#| message: false</span></span>
<span id="cb69-23"><a href="#cb69-23" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(janitor)</span>
<span id="cb69-24"><a href="#cb69-24" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(knitr)</span>
<span id="cb69-25"><a href="#cb69-25" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(broom)</span>
<span id="cb69-26"><a href="#cb69-26" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(glue)</span>
<span id="cb69-27"><a href="#cb69-27" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lme4)</span>
<span id="cb69-28"><a href="#cb69-28" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(mosaic)</span>
<span id="cb69-29"><a href="#cb69-29" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(naniar)</span>
<span id="cb69-30"><a href="#cb69-30" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lubridate)</span>
<span id="cb69-31"><a href="#cb69-31" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb69-32"><a href="#cb69-32" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-33"><a href="#cb69-33" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_bw</span>())</span>
<span id="cb69-34"><a href="#cb69-34" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-35"><a href="#cb69-35" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-36"><a href="#cb69-36" aria-hidden="true" tabindex="-1"></a><span class="fu"># Time to Complete Block Data</span></span>
<span id="cb69-37"><a href="#cb69-37" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-40"><a href="#cb69-40" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-41"><a href="#cb69-41" aria-hidden="true" tabindex="-1"></a>tcb <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(<span class="st">&quot;EMGReflex_Induction_Recovery_TCB-2024-05-27-revised.csv&quot;</span>, </span>
<span id="cb69-42"><a href="#cb69-42" aria-hidden="true" tabindex="-1"></a>                <span class="at">show_col_types =</span> F) <span class="sc">|&gt;</span> </span>
<span id="cb69-43"><a href="#cb69-43" aria-hidden="true" tabindex="-1"></a>  <span class="fu">clean_names</span>() <span class="sc">|&gt;</span></span>
<span id="cb69-44"><a href="#cb69-44" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">date =</span> <span class="fu">mdy</span>(date)) <span class="sc">|&gt;</span></span>
<span id="cb69-45"><a href="#cb69-45" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">pct_ib =</span> <span class="fu">factor</span>(pct_ib),</span>
<span id="cb69-46"><a href="#cb69-46" aria-hidden="true" tabindex="-1"></a>         <span class="at">set =</span> <span class="fu">factor</span>(set),</span>
<span id="cb69-47"><a href="#cb69-47" aria-hidden="true" tabindex="-1"></a>         <span class="at">animal =</span> <span class="fu">factor</span>(animal))</span>
<span id="cb69-48"><a href="#cb69-48" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-49"><a href="#cb69-49" aria-hidden="true" tabindex="-1"></a>tcb_b <span class="ot">&lt;-</span> tcb <span class="sc">|&gt;</span> <span class="fu">filter</span>(time_block <span class="sc">&lt;</span> <span class="dv">300</span>)</span>
<span id="cb69-50"><a href="#cb69-50" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-51"><a href="#cb69-51" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-52"><a href="#cb69-52" aria-hidden="true" tabindex="-1"></a><span class="fu">## Codebook for TCB data</span></span>
<span id="cb69-53"><a href="#cb69-53" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-54"><a href="#cb69-54" aria-hidden="true" tabindex="-1"></a><span class="at">&gt; Block amplitudes of either 40, 60, or 80% of the instantaneous block threshold were applied continuously until complete block was achieved, at which point the block was turned off. The Time to Complete Block (TCB) was recorded in seconds.</span></span>
<span id="cb69-55"><a href="#cb69-55" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-56"><a href="#cb69-56" aria-hidden="true" tabindex="-1"></a>In the original TCB data, we have <span class="in">`r nrow(tcb)`</span> rows and <span class="in">`r ncol(tcb)`</span> variables, with <span class="in">`r n_miss(tcb)`</span> missing observations. </span>
<span id="cb69-57"><a href="#cb69-57" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-58"><a href="#cb69-58" aria-hidden="true" tabindex="-1"></a>The data capture <span class="in">`r n_distinct(tcb$animal)`</span> distinct animals. Each &quot;run&quot; is identified by an <span class="in">`animal`</span>, a <span class="in">`set`</span> (1, 2, or 3) and <span class="in">`date`</span>. Each <span class="in">`animal`</span>-<span class="in">`set`</span> combination yields three runs, all done on the same <span class="in">`date`</span>, with the same <span class="in">`inst_bt`</span>, at the three levels of the experimental setting <span class="in">`pct_ib`</span>, yielding an <span class="in">`abs_block`</span> value specific to the <span class="in">`animal`</span> in that <span class="in">`set`</span> on that <span class="in">`date`</span>.</span>
<span id="cb69-59"><a href="#cb69-59" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-60"><a href="#cb69-60" aria-hidden="true" tabindex="-1"></a>The outcome of interest is time to complete block, measured in seconds. Values of the time to complete block of 300 indicate that complete block was not achieved in 5 minutes.</span>
<span id="cb69-61"><a href="#cb69-61" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-62"><a href="#cb69-62" aria-hidden="true" tabindex="-1"></a>There are <span class="in">`r nrow(tcb |&gt; filter(time_block &lt; 300))`</span> experiments (rows) where the time to complete block was recorded (as less than 300 seconds), and we will restrict some analyses to these experiments.</span>
<span id="cb69-63"><a href="#cb69-63" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-64"><a href="#cb69-64" aria-hidden="true" tabindex="-1"></a>Variable | Description </span>
<span id="cb69-65"><a href="#cb69-65" aria-hidden="true" tabindex="-1"></a>:--------: | :-----------------------------------------------</span>
<span id="cb69-66"><a href="#cb69-66" aria-hidden="true" tabindex="-1"></a><span class="in">`animal`</span> | <span class="in">`r n_distinct(tcb$animal)`</span> different animals</span>
<span id="cb69-67"><a href="#cb69-67" aria-hidden="true" tabindex="-1"></a><span class="in">`date`</span> | between <span class="in">`r min(tcb$date)`</span> and <span class="in">`r max(tcb$date)`</span></span>
<span id="cb69-68"><a href="#cb69-68" aria-hidden="true" tabindex="-1"></a><span class="in">`set`</span> | 1, 2 or 3 (consistent in <span class="in">`date`</span>)</span>
<span id="cb69-69"><a href="#cb69-69" aria-hidden="true" tabindex="-1"></a><span class="in">`inst_bt`</span> | instantaneous BT (mA): consistent within <span class="in">`set`</span> for each <span class="in">`animal`</span></span>
<span id="cb69-70"><a href="#cb69-70" aria-hidden="true" tabindex="-1"></a><span class="in">`pct_ib`</span> | % of instantaneous BT: experimental setting: 40, 60 or 80</span>
<span id="cb69-71"><a href="#cb69-71" aria-hidden="true" tabindex="-1"></a><span class="in">`abs_block`</span> | absolute block value (mA): product of <span class="in">`inst_bt`</span> and <span class="in">`pct_ib`</span></span>
<span id="cb69-72"><a href="#cb69-72" aria-hidden="true" tabindex="-1"></a><span class="in">`time_block`</span> | time to complete block in seconds: quantitative between <span class="in">`r min(tcb$time_block)`</span> and <span class="in">`r max(tcb$time_block)`</span>, with all quantities ending in 2 except for the values originally listed as &gt;300 (here stored as 300.)</span>
<span id="cb69-73"><a href="#cb69-73" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-74"><a href="#cb69-74" aria-hidden="true" tabindex="-1"></a><span class="fu">## Current analytic goals for TCB</span></span>
<span id="cb69-75"><a href="#cb69-75" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-76"><a href="#cb69-76" aria-hidden="true" tabindex="-1"></a>After restricting the TCB data to those <span class="in">`r nrow(tcb |&gt; filter(time_block &lt; 300))`</span> experiments where complete block was achieved in less than 300 seconds,</span>
<span id="cb69-77"><a href="#cb69-77" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-78"><a href="#cb69-78" aria-hidden="true" tabindex="-1"></a><span class="ss">1. </span>Is time to complete block (<span class="in">`time_block`</span>) reasonably well-described using a linear function of <span class="in">`abs_block`</span>?</span>
<span id="cb69-79"><a href="#cb69-79" aria-hidden="true" tabindex="-1"></a><span class="ss">2. </span>If that&#39;s true, then which of the two elements: <span class="in">`inst_bt`</span> or <span class="in">`pct_ib`</span> (whose product is equal to <span class="in">`abs_block`</span>) is the driver of that effect? Or is it truly the combination?</span>
<span id="cb69-80"><a href="#cb69-80" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-81"><a href="#cb69-81" aria-hidden="true" tabindex="-1"></a><span class="fu"># TCB vs. `abs_block`, overall</span></span>
<span id="cb69-82"><a href="#cb69-82" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-83"><a href="#cb69-83" aria-hidden="true" tabindex="-1"></a>Is time to complete block (<span class="in">`time_block`</span>) reasonably well-described using a linear function of <span class="in">`abs_block`</span>?</span>
<span id="cb69-84"><a href="#cb69-84" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-85"><a href="#cb69-85" aria-hidden="true" tabindex="-1"></a><span class="fu">## Plot 1 (Linear Fit)</span></span>
<span id="cb69-86"><a href="#cb69-86" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-87"><a href="#cb69-87" aria-hidden="true" tabindex="-1"></a>This plot excludes the experiments where <span class="in">`time_block`</span> is 300 seconds or more.</span>
<span id="cb69-88"><a href="#cb69-88" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-91"><a href="#cb69-91" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-92"><a href="#cb69-92" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcb_b, <span class="fu">aes</span>(<span class="at">x =</span> abs_block, <span class="at">y =</span> time_block)) <span class="sc">+</span></span>
<span id="cb69-93"><a href="#cb69-93" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb69-94"><a href="#cb69-94" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, <span class="at">formula =</span> y <span class="sc">~</span> x) <span class="sc">+</span></span>
<span id="cb69-95"><a href="#cb69-95" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Block, by Absolute Block Value&quot;</span>,</span>
<span id="cb69-96"><a href="#cb69-96" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">glue</span>(<span class="fu">nrow</span>(tcb_b), <span class="st">&quot; experiments which achieved complete block&quot;</span>),</span>
<span id="cb69-97"><a href="#cb69-97" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Absolute Block Value (mA)&quot;</span>,</span>
<span id="cb69-98"><a href="#cb69-98" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Block (seconds)&quot;</span>)</span>
<span id="cb69-99"><a href="#cb69-99" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-100"><a href="#cb69-100" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot1.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span>
<span id="cb69-101"><a href="#cb69-101" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-102"><a href="#cb69-102" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-103"><a href="#cb69-103" aria-hidden="true" tabindex="-1"></a>The linear model shown here is summarized below:</span>
<span id="cb69-104"><a href="#cb69-104" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-107"><a href="#cb69-107" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-108"><a href="#cb69-108" aria-hidden="true" tabindex="-1"></a>m1 <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_block <span class="sc">~</span> abs_block, <span class="at">data =</span> tcb_b)</span>
<span id="cb69-109"><a href="#cb69-109" aria-hidden="true" tabindex="-1"></a>m1t <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m1, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb69-110"><a href="#cb69-110" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb69-111"><a href="#cb69-111" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-112"><a href="#cb69-112" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m1t, <span class="at">digits =</span> <span class="dv">3</span>)</span>
<span id="cb69-113"><a href="#cb69-113" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-114"><a href="#cb69-114" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m1) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb69-115"><a href="#cb69-115" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb69-116"><a href="#cb69-116" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span>
<span id="cb69-117"><a href="#cb69-117" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-118"><a href="#cb69-118" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-119"><a href="#cb69-119" aria-hidden="true" tabindex="-1"></a><span class="fu">## NEW! Plot 1A. (Grouped)</span></span>
<span id="cb69-120"><a href="#cb69-120" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-121"><a href="#cb69-121" aria-hidden="true" tabindex="-1"></a>Again, this plot excludes the experiments where <span class="in">`time_block`</span> is 300 seconds or more.</span>
<span id="cb69-122"><a href="#cb69-122" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-125"><a href="#cb69-125" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-126"><a href="#cb69-126" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcb_b, <span class="fu">aes</span>(<span class="at">x =</span> abs_block, <span class="at">y =</span> time_block, <span class="at">col =</span> pct_ib, <span class="at">group =</span> pct_ib)) <span class="sc">+</span></span>
<span id="cb69-127"><a href="#cb69-127" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb69-128"><a href="#cb69-128" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, <span class="at">formula =</span> y <span class="sc">~</span> x) <span class="sc">+</span></span>
<span id="cb69-129"><a href="#cb69-129" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Block, by Absolute Block Value&quot;</span>,</span>
<span id="cb69-130"><a href="#cb69-130" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">glue</span>(<span class="fu">nrow</span>(tcb_b), <span class="st">&quot; experiments which achieved complete block&quot;</span>),</span>
<span id="cb69-131"><a href="#cb69-131" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Absolute Block Value (mA)&quot;</span>,</span>
<span id="cb69-132"><a href="#cb69-132" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Block (seconds)&quot;</span>)</span>
<span id="cb69-133"><a href="#cb69-133" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-134"><a href="#cb69-134" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot1a.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span>
<span id="cb69-135"><a href="#cb69-135" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-136"><a href="#cb69-136" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-137"><a href="#cb69-137" aria-hidden="true" tabindex="-1"></a><span class="fu">## Plot 1B. (Quadratic Fit)</span></span>
<span id="cb69-138"><a href="#cb69-138" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-139"><a href="#cb69-139" aria-hidden="true" tabindex="-1"></a>This plot excludes the experiments where <span class="in">`time_block`</span> is 300 seconds or more.</span>
<span id="cb69-140"><a href="#cb69-140" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-143"><a href="#cb69-143" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-144"><a href="#cb69-144" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcb_b, <span class="fu">aes</span>(<span class="at">x =</span> abs_block, <span class="at">y =</span> time_block)) <span class="sc">+</span></span>
<span id="cb69-145"><a href="#cb69-145" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb69-146"><a href="#cb69-146" aria-hidden="true" tabindex="-1"></a>  <span class="fu">stat_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, </span>
<span id="cb69-147"><a href="#cb69-147" aria-hidden="true" tabindex="-1"></a>              <span class="at">formula =</span> y <span class="sc">~</span> x <span class="sc">+</span> <span class="fu">I</span>(x<span class="sc">^</span><span class="dv">2</span>), <span class="at">col =</span> <span class="st">&quot;red&quot;</span>) <span class="sc">+</span></span>
<span id="cb69-148"><a href="#cb69-148" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Block, by Absolute Block Value&quot;</span>,</span>
<span id="cb69-149"><a href="#cb69-149" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">glue</span>(<span class="fu">nrow</span>(tcb_b), <span class="st">&quot; experiments which achieved complete block&quot;</span>),</span>
<span id="cb69-150"><a href="#cb69-150" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Absolute Block Value (mA)&quot;</span>,</span>
<span id="cb69-151"><a href="#cb69-151" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Block (seconds)&quot;</span>)</span>
<span id="cb69-152"><a href="#cb69-152" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-153"><a href="#cb69-153" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot1b.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span>
<span id="cb69-154"><a href="#cb69-154" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-155"><a href="#cb69-155" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-156"><a href="#cb69-156" aria-hidden="true" tabindex="-1"></a>The quadratic model shown in plot 1b is summarized below:</span>
<span id="cb69-157"><a href="#cb69-157" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-160"><a href="#cb69-160" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-161"><a href="#cb69-161" aria-hidden="true" tabindex="-1"></a>m1b <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_block <span class="sc">~</span> abs_block <span class="sc">+</span> <span class="fu">I</span>(abs_block<span class="sc">^</span><span class="dv">2</span>), <span class="at">data =</span> tcb_b)</span>
<span id="cb69-162"><a href="#cb69-162" aria-hidden="true" tabindex="-1"></a>m1bt <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m1b, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb69-163"><a href="#cb69-163" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb69-164"><a href="#cb69-164" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-165"><a href="#cb69-165" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m1bt, <span class="at">digits =</span> <span class="dv">3</span>)</span>
<span id="cb69-166"><a href="#cb69-166" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-167"><a href="#cb69-167" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m1b) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb69-168"><a href="#cb69-168" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb69-169"><a href="#cb69-169" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span>
<span id="cb69-170"><a href="#cb69-170" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-171"><a href="#cb69-171" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-172"><a href="#cb69-172" aria-hidden="true" tabindex="-1"></a><span class="fu">## Plot 1C. Comparing the Linear to the Quadratic Fit</span></span>
<span id="cb69-173"><a href="#cb69-173" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-174"><a href="#cb69-174" aria-hidden="true" tabindex="-1"></a>As we saw above, the *p* value for adding the quadratic term is about 0.096.</span>
<span id="cb69-175"><a href="#cb69-175" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-178"><a href="#cb69-178" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-179"><a href="#cb69-179" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcb_b, <span class="fu">aes</span>(<span class="at">x =</span> abs_block, <span class="at">y =</span> time_block)) <span class="sc">+</span></span>
<span id="cb69-180"><a href="#cb69-180" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb69-181"><a href="#cb69-181" aria-hidden="true" tabindex="-1"></a>  <span class="fu">stat_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, </span>
<span id="cb69-182"><a href="#cb69-182" aria-hidden="true" tabindex="-1"></a>              <span class="at">formula =</span> y <span class="sc">~</span> x <span class="sc">+</span> <span class="fu">I</span>(x<span class="sc">^</span><span class="dv">2</span>), <span class="at">col =</span> <span class="st">&quot;red&quot;</span>) <span class="sc">+</span></span>
<span id="cb69-183"><a href="#cb69-183" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, <span class="at">formula =</span> y <span class="sc">~</span> x, <span class="at">col =</span> <span class="st">&quot;blue&quot;</span>) <span class="sc">+</span></span>
<span id="cb69-184"><a href="#cb69-184" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Block, by Absolute Block Value&quot;</span>,</span>
<span id="cb69-185"><a href="#cb69-185" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">glue</span>(<span class="fu">nrow</span>(tcb_b), <span class="st">&quot; experiments which achieved complete block&quot;</span>),</span>
<span id="cb69-186"><a href="#cb69-186" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Absolute Block Value (mA)&quot;</span>,</span>
<span id="cb69-187"><a href="#cb69-187" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Block (seconds)&quot;</span>)</span>
<span id="cb69-188"><a href="#cb69-188" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-189"><a href="#cb69-189" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot1c.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span>
<span id="cb69-190"><a href="#cb69-190" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-191"><a href="#cb69-191" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-192"><a href="#cb69-192" aria-hidden="true" tabindex="-1"></a><span class="fu">## Conclusions</span></span>
<span id="cb69-193"><a href="#cb69-193" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-194"><a href="#cb69-194" aria-hidden="true" tabindex="-1"></a>Plot 1B shows a slightly more satisfying curve in some regards than does the straight line model shown in Plot 1, but the improvement is associated with a *p* value of 0.096, so the improvement is not substantial enough, given this sample size, for us to clearly select the more complex quadratic fit.</span>
<span id="cb69-195"><a href="#cb69-195" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-196"><a href="#cb69-196" aria-hidden="true" tabindex="-1"></a>So I&#39;ll proceed assuming a linear relationship between absolute block value and time to complete block.</span>
<span id="cb69-197"><a href="#cb69-197" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-198"><a href="#cb69-198" aria-hidden="true" tabindex="-1"></a><span class="fu">## NEW! Plot 1D. (Histogram)</span></span>
<span id="cb69-199"><a href="#cb69-199" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-202"><a href="#cb69-202" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-203"><a href="#cb69-203" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcb, <span class="fu">aes</span>(<span class="at">x =</span> time_block<span class="sc">/</span><span class="dv">60</span>, <span class="at">group =</span> pct_ib)) <span class="sc">+</span></span>
<span id="cb69-204"><a href="#cb69-204" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_histogram</span>(<span class="fu">aes</span>(<span class="at">fill =</span> pct_ib), <span class="at">col =</span> <span class="st">&quot;white&quot;</span>, <span class="at">binwidth =</span> <span class="dv">1</span>) <span class="sc">+</span></span>
<span id="cb69-205"><a href="#cb69-205" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_histogram</span>(<span class="at">data =</span> tcb <span class="sc">|&gt;</span> <span class="fu">filter</span>(time_block <span class="sc">==</span> <span class="dv">300</span>), <span class="at">binwidth =</span> <span class="dv">1</span>, <span class="at">fill =</span> <span class="st">&quot;grey30&quot;</span>) <span class="sc">+</span></span>
<span id="cb69-206"><a href="#cb69-206" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color =</span> <span class="st">&quot;none&quot;</span>, <span class="at">fill =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb69-207"><a href="#cb69-207" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>( <span class="sc">~</span> pct_ib) <span class="sc">+</span></span>
<span id="cb69-208"><a href="#cb69-208" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Block, by % of Instantaneous BT&quot;</span>,</span>
<span id="cb69-209"><a href="#cb69-209" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="st">&quot;n = 16 runs at each % Instantaneous BT level&quot;</span>,</span>
<span id="cb69-210"><a href="#cb69-210" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption =</span> <span class="st">&quot;Failure to block shown in dark grey, at 5 minutes&quot;</span>,</span>
<span id="cb69-211"><a href="#cb69-211" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Time to Complete Block (minutes)&quot;</span>,</span>
<span id="cb69-212"><a href="#cb69-212" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Number of Experiments&quot;</span>)</span>
<span id="cb69-213"><a href="#cb69-213" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-214"><a href="#cb69-214" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot1d.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span>
<span id="cb69-215"><a href="#cb69-215" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-216"><a href="#cb69-216" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-217"><a href="#cb69-217" aria-hidden="true" tabindex="-1"></a><span class="fu"># TCB stratified by % of instantaneous BT groups</span></span>
<span id="cb69-218"><a href="#cb69-218" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-219"><a href="#cb69-219" aria-hidden="true" tabindex="-1"></a><span class="fu">## Plot 2 (time to block vs. instantaneous BT, by % BT)</span></span>
<span id="cb69-220"><a href="#cb69-220" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-221"><a href="#cb69-221" aria-hidden="true" tabindex="-1"></a>This plot gets at whether both instantaneous BT (<span class="in">`inst_bt`</span>) and the percentage of instantaneous BT applied (<span class="in">`pct_ib`</span>) are important parts of the model to predict <span class="in">`time_block`</span>.</span>
<span id="cb69-222"><a href="#cb69-222" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-225"><a href="#cb69-225" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-226"><a href="#cb69-226" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcb_b, <span class="fu">aes</span>(<span class="at">x =</span> inst_bt, <span class="at">y =</span> time_block, <span class="at">col =</span> pct_ib)) <span class="sc">+</span></span>
<span id="cb69-227"><a href="#cb69-227" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb69-228"><a href="#cb69-228" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">formula =</span> y <span class="sc">~</span> x, <span class="at">se =</span> <span class="cn">FALSE</span>) <span class="sc">+</span></span>
<span id="cb69-229"><a href="#cb69-229" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span> pct_ib, <span class="at">labeller =</span> <span class="st">&quot;label_both&quot;</span>) <span class="sc">+</span></span>
<span id="cb69-230"><a href="#cb69-230" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb69-231"><a href="#cb69-231" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Block, by Instantaneous BT (mA)&quot;</span>,</span>
<span id="cb69-232"><a href="#cb69-232" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="st">&quot;Stratified by % of Instantaneous BT&quot;</span>,</span>
<span id="cb69-233"><a href="#cb69-233" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption =</span> <span class="st">&quot;Experiments where Complete Block was not achieved are excluded&quot;</span>,</span>
<span id="cb69-234"><a href="#cb69-234" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Instantaneous BT (mA)&quot;</span>,</span>
<span id="cb69-235"><a href="#cb69-235" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Block (seconds)&quot;</span>)</span>
<span id="cb69-236"><a href="#cb69-236" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-237"><a href="#cb69-237" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot2.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span>
<span id="cb69-238"><a href="#cb69-238" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-239"><a href="#cb69-239" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-240"><a href="#cb69-240" aria-hidden="true" tabindex="-1"></a><span class="fu">## Model for Plot 2</span></span>
<span id="cb69-241"><a href="#cb69-241" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-244"><a href="#cb69-244" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-245"><a href="#cb69-245" aria-hidden="true" tabindex="-1"></a>m2 <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_block <span class="sc">~</span> inst_bt <span class="sc">+</span> pct_ib, <span class="at">data =</span> tcb_b)</span>
<span id="cb69-246"><a href="#cb69-246" aria-hidden="true" tabindex="-1"></a>m2t <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m2, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb69-247"><a href="#cb69-247" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb69-248"><a href="#cb69-248" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-249"><a href="#cb69-249" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m2t, <span class="at">digits =</span> <span class="dv">3</span>)</span>
<span id="cb69-250"><a href="#cb69-250" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-251"><a href="#cb69-251" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m2) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb69-252"><a href="#cb69-252" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb69-253"><a href="#cb69-253" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span>
<span id="cb69-254"><a href="#cb69-254" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-255"><a href="#cb69-255" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-256"><a href="#cb69-256" aria-hidden="true" tabindex="-1"></a><span class="fu">## What is the effect of adding `pct_ib` to a model with just `inst_bt`?</span></span>
<span id="cb69-257"><a href="#cb69-257" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-260"><a href="#cb69-260" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-261"><a href="#cb69-261" aria-hidden="true" tabindex="-1"></a>m2a <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_block <span class="sc">~</span> inst_bt, <span class="at">data =</span> tcb_b)</span>
<span id="cb69-262"><a href="#cb69-262" aria-hidden="true" tabindex="-1"></a>m2at <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m2a, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb69-263"><a href="#cb69-263" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb69-264"><a href="#cb69-264" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-265"><a href="#cb69-265" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m2at, <span class="at">digits =</span> <span class="dv">3</span>)</span>
<span id="cb69-266"><a href="#cb69-266" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-267"><a href="#cb69-267" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m2a) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb69-268"><a href="#cb69-268" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb69-269"><a href="#cb69-269" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span>
<span id="cb69-270"><a href="#cb69-270" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-271"><a href="#cb69-271" aria-hidden="true" tabindex="-1"></a><span class="fu">anova</span>(m2a, m2)</span>
<span id="cb69-272"><a href="#cb69-272" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-273"><a href="#cb69-273" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-274"><a href="#cb69-274" aria-hidden="true" tabindex="-1"></a>Model 2a tells us that <span class="in">`inst_bt`</span>, by itself, has a meaningful effect (p = 0.004) on time to complete block across these 38 experiments. The equation is </span>
<span id="cb69-275"><a href="#cb69-275" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-276"><a href="#cb69-276" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>**time to block = 149.5 - 35.1 (`inst_bt`)**, </span>
<span id="cb69-277"><a href="#cb69-277" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-278"><a href="#cb69-278" aria-hidden="true" tabindex="-1"></a>with a 95% CI for the slope of (-58.5, -11.8).</span>
<span id="cb69-279"><a href="#cb69-279" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-280"><a href="#cb69-280" aria-hidden="true" tabindex="-1"></a>The ANOVA table tells us that adding the <span class="in">`pct_ib`</span> category (40, 60 or 80) adds meaningful predictive value to Model 2a, with an associated *p* value of 0.001.</span>
<span id="cb69-281"><a href="#cb69-281" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-282"><a href="#cb69-282" aria-hidden="true" tabindex="-1"></a>Specifically this yields model 2, which is </span>
<span id="cb69-283"><a href="#cb69-283" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-284"><a href="#cb69-284" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>**time to block = 211.7 - 37.7 (`inst_bt`) - 46.4 (`pct_ib` = 60) - 95.5 (`pct_ib` = 80)**, </span>
<span id="cb69-285"><a href="#cb69-285" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-286"><a href="#cb69-286" aria-hidden="true" tabindex="-1"></a><span class="fu">## NEW! R-square values for predicting TCB</span></span>
<span id="cb69-287"><a href="#cb69-287" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-288"><a href="#cb69-288" aria-hidden="true" tabindex="-1"></a>Using the categorical version of <span class="in">`pct_ib`</span>...</span>
<span id="cb69-289"><a href="#cb69-289" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-290"><a href="#cb69-290" aria-hidden="true" tabindex="-1"></a>Model | R-square | p for <span class="in">`inst_bt`</span> | p for <span class="in">`pct_ib`</span> | p for model</span>
<span id="cb69-291"><a href="#cb69-291" aria-hidden="true" tabindex="-1"></a>---------------: | --------: | -----: | -----: | -----:</span>
<span id="cb69-292"><a href="#cb69-292" aria-hidden="true" tabindex="-1"></a>Model for TCB using <span class="in">`inst_bt`</span> and <span class="in">`pct_ib`</span> | 0.459 | 0.005 | 0.001 | &lt; 0.001</span>
<span id="cb69-293"><a href="#cb69-293" aria-hidden="true" tabindex="-1"></a>Model including only <span class="in">`inst_bt`</span> | 0.205 | 0.004 | -- | 0.004</span>
<span id="cb69-294"><a href="#cb69-294" aria-hidden="true" tabindex="-1"></a>Model including only <span class="in">`pct_ib`</span> | 0.223 | -- | 0.012 | 0.012</span>
<span id="cb69-295"><a href="#cb69-295" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-296"><a href="#cb69-296" aria-hidden="true" tabindex="-1"></a><span class="fu">## Model treating both `inst_bt` and `pct_ib` as numeric</span></span>
<span id="cb69-297"><a href="#cb69-297" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-298"><a href="#cb69-298" aria-hidden="true" tabindex="-1"></a>If instead, we wanted to treat both <span class="in">`inst_bt`</span> and <span class="in">`pct_ib`</span> as numeric rather than as categories, we&#39;d have model 2B, as shown below.</span>
<span id="cb69-299"><a href="#cb69-299" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-302"><a href="#cb69-302" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-303"><a href="#cb69-303" aria-hidden="true" tabindex="-1"></a>m2B <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_block <span class="sc">~</span> inst_bt <span class="sc">+</span> <span class="fu">as.numeric</span>(pct_ib), <span class="at">data =</span> tcb_b)</span>
<span id="cb69-304"><a href="#cb69-304" aria-hidden="true" tabindex="-1"></a>m2Bt <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m2B, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb69-305"><a href="#cb69-305" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb69-306"><a href="#cb69-306" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-307"><a href="#cb69-307" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m2Bt, <span class="at">digits =</span> <span class="dv">3</span>)</span>
<span id="cb69-308"><a href="#cb69-308" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-309"><a href="#cb69-309" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m2B) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb69-310"><a href="#cb69-310" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb69-311"><a href="#cb69-311" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span>
<span id="cb69-312"><a href="#cb69-312" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-313"><a href="#cb69-313" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-314"><a href="#cb69-314" aria-hidden="true" tabindex="-1"></a>This yields a p value &lt; 0.001 for adding <span class="in">`inst_bt`</span> to a model with <span class="in">`pct_ib`</span> alone, and the same p value (&lt; 0.001) for adding <span class="in">`pct_ib`</span> to a model with <span class="in">`inst_bt`</span> alone. In other words, we need both elements of the product.</span>
<span id="cb69-315"><a href="#cb69-315" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-316"><a href="#cb69-316" aria-hidden="true" tabindex="-1"></a><span class="fu">## Plot 3 (*including* the cases where complete block was not achieved)</span></span>
<span id="cb69-317"><a href="#cb69-317" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-318"><a href="#cb69-318" aria-hidden="true" tabindex="-1"></a>I am retaining this plot in case you want to be able to show the values of <span class="in">`pct_ib`</span> and <span class="in">`abs_block`</span> for the experiments which failed to achieve a complete block.</span>
<span id="cb69-319"><a href="#cb69-319" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-322"><a href="#cb69-322" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-323"><a href="#cb69-323" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcb, <span class="fu">aes</span>(<span class="at">x =</span> abs_block, <span class="at">y =</span> time_block, <span class="at">col =</span> pct_ib)) <span class="sc">+</span></span>
<span id="cb69-324"><a href="#cb69-324" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">data =</span> tcb <span class="sc">|&gt;</span> <span class="fu">filter</span>(time_block <span class="sc">&lt;</span> <span class="dv">300</span>), <span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb69-325"><a href="#cb69-325" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">data =</span> tcb <span class="sc">|&gt;</span> <span class="fu">filter</span>(time_block <span class="sc">==</span> <span class="dv">300</span>), </span>
<span id="cb69-326"><a href="#cb69-326" aria-hidden="true" tabindex="-1"></a>             <span class="fu">aes</span>(<span class="at">fill =</span> pct_ib), <span class="at">shape =</span> <span class="st">&quot;X&quot;</span>, <span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb69-327"><a href="#cb69-327" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span> pct_ib, <span class="at">labeller =</span> <span class="st">&quot;label_both&quot;</span>) <span class="sc">+</span></span>
<span id="cb69-328"><a href="#cb69-328" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color =</span> <span class="st">&quot;none&quot;</span>, <span class="at">fill =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb69-329"><a href="#cb69-329" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Block, by Absolute Block Value&quot;</span>,</span>
<span id="cb69-330"><a href="#cb69-330" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="st">&quot;Stratified by % of Instantaneous BT&quot;</span>,</span>
<span id="cb69-331"><a href="#cb69-331" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption =</span> <span class="st">&quot;Experiments where Complete Block was not achieved indicated by X at Time = 300 seconds&quot;</span>,</span>
<span id="cb69-332"><a href="#cb69-332" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Absolute Block Value (mA)&quot;</span>,</span>
<span id="cb69-333"><a href="#cb69-333" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Block (seconds)&quot;</span>)</span>
<span id="cb69-334"><a href="#cb69-334" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-335"><a href="#cb69-335" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot3.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span>
<span id="cb69-336"><a href="#cb69-336" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-337"><a href="#cb69-337" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-338"><a href="#cb69-338" aria-hidden="true" tabindex="-1"></a><span class="fu">## Plot 3A (*excluding* the cases where complete block was not achieved)</span></span>
<span id="cb69-339"><a href="#cb69-339" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-340"><a href="#cb69-340" aria-hidden="true" tabindex="-1"></a>Plot 3A is the one we&#39;ve seen before, which looks at separate lines predicting time to complete block, by absolute block value.</span>
<span id="cb69-341"><a href="#cb69-341" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-344"><a href="#cb69-344" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-345"><a href="#cb69-345" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcb_b, <span class="fu">aes</span>(<span class="at">x =</span> abs_block, <span class="at">y =</span> time_block, <span class="at">col =</span> pct_ib)) <span class="sc">+</span></span>
<span id="cb69-346"><a href="#cb69-346" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb69-347"><a href="#cb69-347" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">formula =</span> y <span class="sc">~</span> x, <span class="at">se =</span> <span class="cn">FALSE</span>) <span class="sc">+</span></span>
<span id="cb69-348"><a href="#cb69-348" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span> pct_ib, <span class="at">labeller =</span> <span class="st">&quot;label_both&quot;</span>) <span class="sc">+</span></span>
<span id="cb69-349"><a href="#cb69-349" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb69-350"><a href="#cb69-350" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Block, by Absolute Block Value&quot;</span>,</span>
<span id="cb69-351"><a href="#cb69-351" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="st">&quot;Stratified by % of Instantaneous BT&quot;</span>,</span>
<span id="cb69-352"><a href="#cb69-352" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption =</span> <span class="st">&quot;Experiments where Complete Block was not achieved are excluded&quot;</span>,</span>
<span id="cb69-353"><a href="#cb69-353" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Absolute Block Value (mA)&quot;</span>,</span>
<span id="cb69-354"><a href="#cb69-354" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Block (seconds)&quot;</span>)</span>
<span id="cb69-355"><a href="#cb69-355" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-356"><a href="#cb69-356" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot3a.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span>
<span id="cb69-357"><a href="#cb69-357" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-358"><a href="#cb69-358" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-359"><a href="#cb69-359" aria-hidden="true" tabindex="-1"></a><span class="fu">## Model for Plot 3A</span></span>
<span id="cb69-360"><a href="#cb69-360" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-363"><a href="#cb69-363" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-364"><a href="#cb69-364" aria-hidden="true" tabindex="-1"></a>m3a <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_block <span class="sc">~</span> abs_block <span class="sc">+</span> pct_ib, <span class="at">data =</span> tcb_b)</span>
<span id="cb69-365"><a href="#cb69-365" aria-hidden="true" tabindex="-1"></a>m3at <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m3a, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb69-366"><a href="#cb69-366" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb69-367"><a href="#cb69-367" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-368"><a href="#cb69-368" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m3at, <span class="at">digits =</span> <span class="dv">3</span>)</span>
<span id="cb69-369"><a href="#cb69-369" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-370"><a href="#cb69-370" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m3a) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb69-371"><a href="#cb69-371" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb69-372"><a href="#cb69-372" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span>
<span id="cb69-373"><a href="#cb69-373" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-374"><a href="#cb69-374" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-375"><a href="#cb69-375" aria-hidden="true" tabindex="-1"></a>I&#39;m not sure whether this model is still relevant. I think we now prefer the model for Plot 2.</span>
<span id="cb69-376"><a href="#cb69-376" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-377"><a href="#cb69-377" aria-hidden="true" tabindex="-1"></a><span class="fu"># Time to Complete Recovery Data</span></span>
<span id="cb69-378"><a href="#cb69-378" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-381"><a href="#cb69-381" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-382"><a href="#cb69-382" aria-hidden="true" tabindex="-1"></a>tcr <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(<span class="st">&quot;EMGReflex_Induction_Recovery_TCR-2024-05-27_revised.csv&quot;</span>, </span>
<span id="cb69-383"><a href="#cb69-383" aria-hidden="true" tabindex="-1"></a>                <span class="at">show_col_types =</span> F) <span class="sc">|&gt;</span> </span>
<span id="cb69-384"><a href="#cb69-384" aria-hidden="true" tabindex="-1"></a>  <span class="fu">clean_names</span>() <span class="sc">|&gt;</span></span>
<span id="cb69-385"><a href="#cb69-385" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">date =</span> <span class="fu">mdy</span>(date)) <span class="sc">|&gt;</span></span>
<span id="cb69-386"><a href="#cb69-386" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">set =</span> <span class="fu">factor</span>(set),</span>
<span id="cb69-387"><a href="#cb69-387" aria-hidden="true" tabindex="-1"></a>         <span class="at">charge =</span> <span class="fu">as.numeric</span>(block_period <span class="sc">*</span> bt),</span>
<span id="cb69-388"><a href="#cb69-388" aria-hidden="true" tabindex="-1"></a>         <span class="at">block_period =</span> <span class="fu">factor</span>(block_period),</span>
<span id="cb69-389"><a href="#cb69-389" aria-hidden="true" tabindex="-1"></a>         <span class="at">animal =</span> <span class="fu">factor</span>(animal))</span>
<span id="cb69-390"><a href="#cb69-390" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-391"><a href="#cb69-391" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-392"><a href="#cb69-392" aria-hidden="true" tabindex="-1"></a><span class="fu">## Codebook for TCR data</span></span>
<span id="cb69-393"><a href="#cb69-393" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-394"><a href="#cb69-394" aria-hidden="true" tabindex="-1"></a>We have <span class="in">`r nrow(tcr)`</span> rows and <span class="in">`r ncol(tcr)`</span> variables, with <span class="in">`r n_miss(tcr)`</span> missing observations.</span>
<span id="cb69-395"><a href="#cb69-395" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-396"><a href="#cb69-396" aria-hidden="true" tabindex="-1"></a>Variable | Description </span>
<span id="cb69-397"><a href="#cb69-397" aria-hidden="true" tabindex="-1"></a>:--------: | :-----------------------------------------------</span>
<span id="cb69-398"><a href="#cb69-398" aria-hidden="true" tabindex="-1"></a><span class="in">`animal`</span> | EMG_Reflex 086, 092, 094, 095, 106 and 109</span>
<span id="cb69-399"><a href="#cb69-399" aria-hidden="true" tabindex="-1"></a><span class="in">`date`</span> | between 2022-11-22 and 2023-06-06</span>
<span id="cb69-400"><a href="#cb69-400" aria-hidden="true" tabindex="-1"></a><span class="in">`set`</span> | 1, 2 or 3 (consistent in <span class="in">`date`</span>)</span>
<span id="cb69-401"><a href="#cb69-401" aria-hidden="true" tabindex="-1"></a><span class="in">`bt`</span> | BT (mA): consistent within <span class="in">`set`</span> for each <span class="in">`animal`</span></span>
<span id="cb69-402"><a href="#cb69-402" aria-hidden="true" tabindex="-1"></a><span class="in">`block_period`</span> | Block period (in minutes): 1, 2 or 3</span>
<span id="cb69-403"><a href="#cb69-403" aria-hidden="true" tabindex="-1"></a><span class="in">`time_recover`</span> | Time to Complete Recovery (seconds): quantitative between 2 and 592 with all quantities ending in 2.</span>
<span id="cb69-404"><a href="#cb69-404" aria-hidden="true" tabindex="-1"></a><span class="in">`charge`</span> | <span class="in">`block_period`</span> times <span class="in">`bt`</span></span>
<span id="cb69-405"><a href="#cb69-405" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-406"><a href="#cb69-406" aria-hidden="true" tabindex="-1"></a>Each &quot;run&quot; is identified by an <span class="in">`animal`</span>, a <span class="in">`set`</span> (1, 2, or 3) and <span class="in">`date`</span>. Each <span class="in">`animal`</span>-<span class="in">`set`</span> combination yields three runs, all done on the same <span class="in">`date`</span>, with the same <span class="in">`bt`</span>, at the three levels of the experimental setting <span class="in">`block_period`</span>.</span>
<span id="cb69-407"><a href="#cb69-407" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-408"><a href="#cb69-408" aria-hidden="true" tabindex="-1"></a>The outcome of interest is time to complete recovery, measured in seconds.</span>
<span id="cb69-409"><a href="#cb69-409" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-410"><a href="#cb69-410" aria-hidden="true" tabindex="-1"></a><span class="fu">## Current analytic goals for TCR</span></span>
<span id="cb69-411"><a href="#cb69-411" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-412"><a href="#cb69-412" aria-hidden="true" tabindex="-1"></a>Using all of the TCR data,</span>
<span id="cb69-413"><a href="#cb69-413" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-414"><a href="#cb69-414" aria-hidden="true" tabindex="-1"></a><span class="ss">1. </span>Is time to recovery (<span class="in">`time_recover`</span>) reasonably well-described using a linear function of <span class="in">`charge`</span>?</span>
<span id="cb69-415"><a href="#cb69-415" aria-hidden="true" tabindex="-1"></a><span class="ss">2. </span>If that&#39;s true, then which of the two elements: <span class="in">`block_period`</span> or <span class="in">`bt`</span> (whose product is equal to <span class="in">`charge`</span>) is the driver of that effect? Or is it truly the combination?</span>
<span id="cb69-416"><a href="#cb69-416" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-417"><a href="#cb69-417" aria-hidden="true" tabindex="-1"></a><span class="fu"># TCR vs. `charge`, overall</span></span>
<span id="cb69-418"><a href="#cb69-418" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-419"><a href="#cb69-419" aria-hidden="true" tabindex="-1"></a>Is time to recovery (<span class="in">`time_recover`</span>) reasonably well-described using a linear function of <span class="in">`charge`</span>?</span>
<span id="cb69-420"><a href="#cb69-420" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-421"><a href="#cb69-421" aria-hidden="true" tabindex="-1"></a><span class="fu">## Plot 4 (Linear Fit)</span></span>
<span id="cb69-422"><a href="#cb69-422" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-423"><a href="#cb69-423" aria-hidden="true" tabindex="-1"></a>This plot includes all <span class="in">`r nrow(tcr)`</span> experiments included in the TCR data.</span>
<span id="cb69-424"><a href="#cb69-424" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-427"><a href="#cb69-427" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-428"><a href="#cb69-428" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcr, <span class="fu">aes</span>(<span class="at">x =</span> charge, <span class="at">y =</span> time_recover)) <span class="sc">+</span></span>
<span id="cb69-429"><a href="#cb69-429" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb69-430"><a href="#cb69-430" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, <span class="at">formula =</span> y <span class="sc">~</span> x) <span class="sc">+</span></span>
<span id="cb69-431"><a href="#cb69-431" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Recovery, by Charge&quot;</span>,</span>
<span id="cb69-432"><a href="#cb69-432" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">glue</span>(<span class="fu">nrow</span>(tcr), <span class="st">&quot; experiments on time to recovery&quot;</span>),</span>
<span id="cb69-433"><a href="#cb69-433" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Charge&quot;</span>,</span>
<span id="cb69-434"><a href="#cb69-434" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Recovery (seconds)&quot;</span>)</span>
<span id="cb69-435"><a href="#cb69-435" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-436"><a href="#cb69-436" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot4.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span>
<span id="cb69-437"><a href="#cb69-437" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-438"><a href="#cb69-438" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-439"><a href="#cb69-439" aria-hidden="true" tabindex="-1"></a>The linear model shown in plot 4 is summarized below:</span>
<span id="cb69-440"><a href="#cb69-440" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-443"><a href="#cb69-443" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-444"><a href="#cb69-444" aria-hidden="true" tabindex="-1"></a>m4 <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_recover <span class="sc">~</span> charge, <span class="at">data =</span> tcr)</span>
<span id="cb69-445"><a href="#cb69-445" aria-hidden="true" tabindex="-1"></a>m4t <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m4, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb69-446"><a href="#cb69-446" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb69-447"><a href="#cb69-447" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-448"><a href="#cb69-448" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m4t, <span class="at">digits =</span> <span class="dv">3</span>)</span>
<span id="cb69-449"><a href="#cb69-449" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-450"><a href="#cb69-450" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m4) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb69-451"><a href="#cb69-451" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb69-452"><a href="#cb69-452" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span>
<span id="cb69-453"><a href="#cb69-453" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-454"><a href="#cb69-454" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-455"><a href="#cb69-455" aria-hidden="true" tabindex="-1"></a><span class="fu">## NEW! Plot 4A. (Grouped)</span></span>
<span id="cb69-456"><a href="#cb69-456" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-457"><a href="#cb69-457" aria-hidden="true" tabindex="-1"></a>Again, this plot includes all <span class="in">`r nrow(tcr)`</span> experiments included in the TCR data.</span>
<span id="cb69-458"><a href="#cb69-458" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-461"><a href="#cb69-461" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-462"><a href="#cb69-462" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcr, <span class="fu">aes</span>(<span class="at">x =</span> charge, <span class="at">y =</span> time_recover, <span class="at">col =</span> block_period, <span class="at">group =</span> block_period)) <span class="sc">+</span></span>
<span id="cb69-463"><a href="#cb69-463" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb69-464"><a href="#cb69-464" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, <span class="at">formula =</span> y <span class="sc">~</span> x) <span class="sc">+</span></span>
<span id="cb69-465"><a href="#cb69-465" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Recovery, by Charge&quot;</span>,</span>
<span id="cb69-466"><a href="#cb69-466" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">glue</span>(<span class="fu">nrow</span>(tcr), <span class="st">&quot; experiments on time to recovery&quot;</span>),</span>
<span id="cb69-467"><a href="#cb69-467" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Charge&quot;</span>,</span>
<span id="cb69-468"><a href="#cb69-468" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Recovery (seconds)&quot;</span>)</span>
<span id="cb69-469"><a href="#cb69-469" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-470"><a href="#cb69-470" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot4a.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span>
<span id="cb69-471"><a href="#cb69-471" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-472"><a href="#cb69-472" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-473"><a href="#cb69-473" aria-hidden="true" tabindex="-1"></a><span class="fu">## Plot 4B. (Quadratic Fit)</span></span>
<span id="cb69-474"><a href="#cb69-474" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-475"><a href="#cb69-475" aria-hidden="true" tabindex="-1"></a>This plot again includes all <span class="in">`r nrow(tcr)`</span> experiments on time to recovery.</span>
<span id="cb69-476"><a href="#cb69-476" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-479"><a href="#cb69-479" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-480"><a href="#cb69-480" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcr, <span class="fu">aes</span>(<span class="at">x =</span> charge, <span class="at">y =</span> time_recover)) <span class="sc">+</span></span>
<span id="cb69-481"><a href="#cb69-481" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb69-482"><a href="#cb69-482" aria-hidden="true" tabindex="-1"></a>  <span class="fu">stat_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, </span>
<span id="cb69-483"><a href="#cb69-483" aria-hidden="true" tabindex="-1"></a>              <span class="at">formula =</span> y <span class="sc">~</span> x <span class="sc">+</span> <span class="fu">I</span>(x<span class="sc">^</span><span class="dv">2</span>), <span class="at">col =</span> <span class="st">&quot;red&quot;</span>) <span class="sc">+</span></span>
<span id="cb69-484"><a href="#cb69-484" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Recovery, by Charge&quot;</span>,</span>
<span id="cb69-485"><a href="#cb69-485" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">glue</span>(<span class="fu">nrow</span>(tcr), <span class="st">&quot; experiments on time to complete recovery&quot;</span>),</span>
<span id="cb69-486"><a href="#cb69-486" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Charge&quot;</span>,</span>
<span id="cb69-487"><a href="#cb69-487" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Recovery (seconds)&quot;</span>)</span>
<span id="cb69-488"><a href="#cb69-488" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-489"><a href="#cb69-489" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot4b.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span>
<span id="cb69-490"><a href="#cb69-490" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-491"><a href="#cb69-491" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-492"><a href="#cb69-492" aria-hidden="true" tabindex="-1"></a>The quadratic model shown in plot 4B is summarized below:</span>
<span id="cb69-493"><a href="#cb69-493" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-496"><a href="#cb69-496" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-497"><a href="#cb69-497" aria-hidden="true" tabindex="-1"></a>m4b <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_recover <span class="sc">~</span> charge <span class="sc">+</span> <span class="fu">I</span>(charge<span class="sc">^</span><span class="dv">2</span>), <span class="at">data =</span> tcr)</span>
<span id="cb69-498"><a href="#cb69-498" aria-hidden="true" tabindex="-1"></a>m4bt <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m4b, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb69-499"><a href="#cb69-499" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb69-500"><a href="#cb69-500" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-501"><a href="#cb69-501" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m4bt, <span class="at">digits =</span> <span class="dv">3</span>)</span>
<span id="cb69-502"><a href="#cb69-502" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-503"><a href="#cb69-503" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m4b) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb69-504"><a href="#cb69-504" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb69-505"><a href="#cb69-505" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span>
<span id="cb69-506"><a href="#cb69-506" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-507"><a href="#cb69-507" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-508"><a href="#cb69-508" aria-hidden="true" tabindex="-1"></a><span class="fu">## Plot 4C. Comparing the Linear to the Quadratic Fit</span></span>
<span id="cb69-509"><a href="#cb69-509" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-510"><a href="#cb69-510" aria-hidden="true" tabindex="-1"></a>As we saw above, the *p* value for adding the quadratic term for Model 4 is about 0.391, so we won&#39;t bother, but here&#39;s the plot comparing the two fits.</span>
<span id="cb69-511"><a href="#cb69-511" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-514"><a href="#cb69-514" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-515"><a href="#cb69-515" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcr, <span class="fu">aes</span>(<span class="at">x =</span> charge, <span class="at">y =</span> time_recover)) <span class="sc">+</span></span>
<span id="cb69-516"><a href="#cb69-516" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb69-517"><a href="#cb69-517" aria-hidden="true" tabindex="-1"></a>  <span class="fu">stat_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, </span>
<span id="cb69-518"><a href="#cb69-518" aria-hidden="true" tabindex="-1"></a>              <span class="at">formula =</span> y <span class="sc">~</span> x <span class="sc">+</span> <span class="fu">I</span>(x<span class="sc">^</span><span class="dv">2</span>), <span class="at">col =</span> <span class="st">&quot;red&quot;</span>) <span class="sc">+</span></span>
<span id="cb69-519"><a href="#cb69-519" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">se =</span> <span class="cn">FALSE</span>, <span class="at">formula =</span> y <span class="sc">~</span> x, <span class="at">col =</span> <span class="st">&quot;blue&quot;</span>) <span class="sc">+</span></span>
<span id="cb69-520"><a href="#cb69-520" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Recovery, by Charge&quot;</span>,</span>
<span id="cb69-521"><a href="#cb69-521" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="fu">glue</span>(<span class="fu">nrow</span>(tcr), <span class="st">&quot; experiments on time to complete recovery&quot;</span>),</span>
<span id="cb69-522"><a href="#cb69-522" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Charge&quot;</span>,</span>
<span id="cb69-523"><a href="#cb69-523" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Recovery (seconds)&quot;</span>)</span>
<span id="cb69-524"><a href="#cb69-524" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-525"><a href="#cb69-525" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot4c.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span>
<span id="cb69-526"><a href="#cb69-526" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-527"><a href="#cb69-527" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-528"><a href="#cb69-528" aria-hidden="true" tabindex="-1"></a><span class="fu">## Conclusions</span></span>
<span id="cb69-529"><a href="#cb69-529" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-530"><a href="#cb69-530" aria-hidden="true" tabindex="-1"></a>Plot 4B isn&#39;t an improvement over the straight line model shown in Plot 4, and the associated *p* value for adding the quadratic term is *p* = 0.391.</span>
<span id="cb69-531"><a href="#cb69-531" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-532"><a href="#cb69-532" aria-hidden="true" tabindex="-1"></a>So I&#39;ll proceed assuming a linear relationship between charge and time to complete recovery.</span>
<span id="cb69-533"><a href="#cb69-533" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-534"><a href="#cb69-534" aria-hidden="true" tabindex="-1"></a><span class="fu"># TCR stratified by Block Period groups</span></span>
<span id="cb69-535"><a href="#cb69-535" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-536"><a href="#cb69-536" aria-hidden="true" tabindex="-1"></a><span class="fu">## Plot 5 (time to recover vs. BT, by block period)</span></span>
<span id="cb69-537"><a href="#cb69-537" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-538"><a href="#cb69-538" aria-hidden="true" tabindex="-1"></a>This plot gets at whether both BT (<span class="in">`bt`</span>) and the block period (in minutes: <span class="in">`block_period`</span>) are important parts of the model to predict <span class="in">`time_recover`</span>.</span>
<span id="cb69-539"><a href="#cb69-539" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-542"><a href="#cb69-542" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-543"><a href="#cb69-543" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(tcr, <span class="fu">aes</span>(<span class="at">x =</span> bt, <span class="at">y =</span> time_recover, <span class="at">col =</span> block_period)) <span class="sc">+</span></span>
<span id="cb69-544"><a href="#cb69-544" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb69-545"><a href="#cb69-545" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method =</span> <span class="st">&quot;lm&quot;</span>, <span class="at">formula =</span> y <span class="sc">~</span> x, <span class="at">se =</span> <span class="cn">FALSE</span>) <span class="sc">+</span></span>
<span id="cb69-546"><a href="#cb69-546" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span> block_period, <span class="at">labeller =</span> <span class="st">&quot;label_both&quot;</span>) <span class="sc">+</span></span>
<span id="cb69-547"><a href="#cb69-547" aria-hidden="true" tabindex="-1"></a>  <span class="fu">guides</span>(<span class="at">color =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb69-548"><a href="#cb69-548" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Time to Complete Recovery, by BT&quot;</span>,</span>
<span id="cb69-549"><a href="#cb69-549" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="st">&quot;Stratified by Block Period (minutes)&quot;</span>,</span>
<span id="cb69-550"><a href="#cb69-550" aria-hidden="true" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;BT (mA)&quot;</span>,</span>
<span id="cb69-551"><a href="#cb69-551" aria-hidden="true" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Time to Complete Recovery (seconds)&quot;</span>)</span>
<span id="cb69-552"><a href="#cb69-552" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-553"><a href="#cb69-553" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">&quot;plots/plot5.pdf&quot;</span>, <span class="at">dpi =</span> <span class="dv">300</span>)</span>
<span id="cb69-554"><a href="#cb69-554" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-555"><a href="#cb69-555" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-556"><a href="#cb69-556" aria-hidden="true" tabindex="-1"></a><span class="fu">## Model for Plot 5</span></span>
<span id="cb69-557"><a href="#cb69-557" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-560"><a href="#cb69-560" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-561"><a href="#cb69-561" aria-hidden="true" tabindex="-1"></a>m5 <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_recover <span class="sc">~</span> bt <span class="sc">+</span> block_period, <span class="at">data =</span> tcr)</span>
<span id="cb69-562"><a href="#cb69-562" aria-hidden="true" tabindex="-1"></a>m5t <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m5, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb69-563"><a href="#cb69-563" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb69-564"><a href="#cb69-564" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-565"><a href="#cb69-565" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m5t, <span class="at">digits =</span> <span class="dv">3</span>)</span>
<span id="cb69-566"><a href="#cb69-566" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-567"><a href="#cb69-567" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m5) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb69-568"><a href="#cb69-568" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb69-569"><a href="#cb69-569" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span>
<span id="cb69-570"><a href="#cb69-570" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-571"><a href="#cb69-571" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-572"><a href="#cb69-572" aria-hidden="true" tabindex="-1"></a><span class="fu">## What is the effect of adding `block_period` to a model with just `bt`?</span></span>
<span id="cb69-573"><a href="#cb69-573" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-576"><a href="#cb69-576" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-577"><a href="#cb69-577" aria-hidden="true" tabindex="-1"></a>m5a <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_recover <span class="sc">~</span> bt, <span class="at">data =</span> tcr)</span>
<span id="cb69-578"><a href="#cb69-578" aria-hidden="true" tabindex="-1"></a>m5at <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m5a, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb69-579"><a href="#cb69-579" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb69-580"><a href="#cb69-580" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-581"><a href="#cb69-581" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m5at, <span class="at">digits =</span> <span class="dv">3</span>)</span>
<span id="cb69-582"><a href="#cb69-582" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-583"><a href="#cb69-583" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m5a) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb69-584"><a href="#cb69-584" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb69-585"><a href="#cb69-585" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span>
<span id="cb69-586"><a href="#cb69-586" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-587"><a href="#cb69-587" aria-hidden="true" tabindex="-1"></a><span class="fu">anova</span>(m5a, m5)</span>
<span id="cb69-588"><a href="#cb69-588" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-589"><a href="#cb69-589" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-590"><a href="#cb69-590" aria-hidden="true" tabindex="-1"></a>Model 5a tells us that <span class="in">`bt`</span>, by itself, has a meaningful effect (p = 0.003) on time to complete recovery across these <span class="in">`r nrow(tcr)`</span> experiments. The equation is </span>
<span id="cb69-591"><a href="#cb69-591" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-592"><a href="#cb69-592" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>**time to recovery = 3.07 + 76.9 (`bt`)**, </span>
<span id="cb69-593"><a href="#cb69-593" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-594"><a href="#cb69-594" aria-hidden="true" tabindex="-1"></a>with a 95% CI for the slope of (28.1, 125.7).</span>
<span id="cb69-595"><a href="#cb69-595" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-596"><a href="#cb69-596" aria-hidden="true" tabindex="-1"></a>The ANOVA table tells us that adding the <span class="in">`block_period`</span> category (1, 2 or 3 minutes) adds meaningful predictive value to Model 5a, with an associated *p* value &lt; 0.001.</span>
<span id="cb69-597"><a href="#cb69-597" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-598"><a href="#cb69-598" aria-hidden="true" tabindex="-1"></a>Specifically this yields model 5, which is </span>
<span id="cb69-599"><a href="#cb69-599" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-600"><a href="#cb69-600" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>**time to recovery = -62.26 + 76.9 (`bt`) + 54 (`block_period` = 2) + 142 (`block_period` = 3)**, </span>
<span id="cb69-601"><a href="#cb69-601" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-602"><a href="#cb69-602" aria-hidden="true" tabindex="-1"></a><span class="fu">## NEW! R-square values for predicting TCR</span></span>
<span id="cb69-603"><a href="#cb69-603" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-604"><a href="#cb69-604" aria-hidden="true" tabindex="-1"></a>Using the categorical version of <span class="in">`block_period`</span>...</span>
<span id="cb69-605"><a href="#cb69-605" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-606"><a href="#cb69-606" aria-hidden="true" tabindex="-1"></a>Model | R-square | p for <span class="in">`bt`</span> | p for <span class="in">`block_period`</span> | p for model</span>
<span id="cb69-607"><a href="#cb69-607" aria-hidden="true" tabindex="-1"></a>---------------: | --------: | -----: | -----: | -----:</span>
<span id="cb69-608"><a href="#cb69-608" aria-hidden="true" tabindex="-1"></a>Model for TCR using <span class="in">`bt`</span> and <span class="in">`block_period`</span> | 0.425 | &lt; 0.001 | &lt; 0.001 | &lt; 0.001</span>
<span id="cb69-609"><a href="#cb69-609" aria-hidden="true" tabindex="-1"></a>Model including only <span class="in">`bt`</span> | 0.190 | 0.003 | -- | 0.003</span>
<span id="cb69-610"><a href="#cb69-610" aria-hidden="true" tabindex="-1"></a>Model including only <span class="in">`block_period`</span> | 0.235 | -- | 0.004 | 0.004</span>
<span id="cb69-611"><a href="#cb69-611" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-612"><a href="#cb69-612" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-613"><a href="#cb69-613" aria-hidden="true" tabindex="-1"></a><span class="fu">## Model treating both `bt` and `block_period` as numeric</span></span>
<span id="cb69-614"><a href="#cb69-614" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-615"><a href="#cb69-615" aria-hidden="true" tabindex="-1"></a>If instead, we wanted to treat both <span class="in">`bt`</span> and <span class="in">`block_period`</span> as numeric rather than as categories, we&#39;d have model 5B, as shown below.</span>
<span id="cb69-616"><a href="#cb69-616" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-619"><a href="#cb69-619" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb69-620"><a href="#cb69-620" aria-hidden="true" tabindex="-1"></a>m5B <span class="ot">&lt;-</span> <span class="fu">lm</span>(time_recover <span class="sc">~</span> bt <span class="sc">+</span> <span class="fu">as.numeric</span>(block_period), <span class="at">data =</span> tcr)</span>
<span id="cb69-621"><a href="#cb69-621" aria-hidden="true" tabindex="-1"></a>m5Bt <span class="ot">&lt;-</span> <span class="fu">tidy</span>(m5B, <span class="at">conf.int =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb69-622"><a href="#cb69-622" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(term, estimate, std.error, p.value, conf.low, conf.high)</span>
<span id="cb69-623"><a href="#cb69-623" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-624"><a href="#cb69-624" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(m5Bt, <span class="at">digits =</span> <span class="dv">3</span>)</span>
<span id="cb69-625"><a href="#cb69-625" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-626"><a href="#cb69-626" aria-hidden="true" tabindex="-1"></a><span class="fu">glance</span>(m5B) <span class="sc">|&gt;</span> <span class="fu">select</span>(r.squared, adj.r.squared, sigma, </span>
<span id="cb69-627"><a href="#cb69-627" aria-hidden="true" tabindex="-1"></a>                     p.value, df, df.residual, nobs) <span class="sc">|&gt;</span></span>
<span id="cb69-628"><a href="#cb69-628" aria-hidden="true" tabindex="-1"></a>  <span class="fu">kable</span>()</span>
<span id="cb69-629"><a href="#cb69-629" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb69-630"><a href="#cb69-630" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb69-631"><a href="#cb69-631" aria-hidden="true" tabindex="-1"></a>This yields a p value = 0.001 for adding <span class="in">`bt`</span> to a model with <span class="in">`block_period`</span> alone, and a p value &lt; 0.001 for adding <span class="in">`block_period`</span> to a model with <span class="in">`bt`</span> alone. Again, we need both elements of the product.</span>
<span id="cb69-632"><a href="#cb69-632" aria-hidden="true" tabindex="-1"></a></span>
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